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Sample records for object recognition system

  1. Cognitive object recognition system (CORS)

    NASA Astrophysics Data System (ADS)

    Raju, Chaitanya; Varadarajan, Karthik Mahesh; Krishnamurthi, Niyant; Xu, Shuli; Biederman, Irving; Kelley, Troy

    2010-04-01

    We have developed a framework, Cognitive Object Recognition System (CORS), inspired by current neurocomputational models and psychophysical research in which multiple recognition algorithms (shape based geometric primitives, 'geons,' and non-geometric feature-based algorithms) are integrated to provide a comprehensive solution to object recognition and landmarking. Objects are defined as a combination of geons, corresponding to their simple parts, and the relations among the parts. However, those objects that are not easily decomposable into geons, such as bushes and trees, are recognized by CORS using "feature-based" algorithms. The unique interaction between these algorithms is a novel approach that combines the effectiveness of both algorithms and takes us closer to a generalized approach to object recognition. CORS allows recognition of objects through a larger range of poses using geometric primitives and performs well under heavy occlusion - about 35% of object surface is sufficient. Furthermore, geon composition of an object allows image understanding and reasoning even with novel objects. With reliable landmarking capability, the system improves vision-based robot navigation in GPS-denied environments. Feasibility of the CORS system was demonstrated with real stereo images captured from a Pioneer robot. The system can currently identify doors, door handles, staircases, trashcans and other relevant landmarks in the indoor environment.

  2. Method and System for Object Recognition Search

    NASA Technical Reports Server (NTRS)

    Duong, Tuan A. (Inventor); Duong, Vu A. (Inventor); Stubberud, Allen R. (Inventor)

    2012-01-01

    A method for object recognition using shape and color features of the object to be recognized. An adaptive architecture is used to recognize and adapt the shape and color features for moving objects to enable object recognition.

  3. A neuromorphic system for video object recognition.

    PubMed

    Khosla, Deepak; Chen, Yang; Kim, Kyungnam

    2014-01-01

    Automated video object recognition is a topic of emerging importance in both defense and civilian applications. This work describes an accurate and low-power neuromorphic architecture and system for real-time automated video object recognition. Our system, Neuormorphic Visual Understanding of Scenes (NEOVUS), is inspired by computational neuroscience models of feed-forward object detection and classification pipelines for processing visual data. The NEOVUS architecture is inspired by the ventral (what) and dorsal (where) streams of the mammalian visual pathway and integrates retinal processing, object detection based on form and motion modeling, and object classification based on convolutional neural networks. The object recognition performance and energy use of the NEOVUS was evaluated by the Defense Advanced Research Projects Agency (DARPA) under the Neovision2 program using three urban area video datasets collected from a mix of stationary and moving platforms. These datasets are challenging and include a large number of objects of different types in cluttered scenes, with varying illumination and occlusion conditions. In a systematic evaluation of five different teams by DARPA on these datasets, the NEOVUS demonstrated the best performance with high object recognition accuracy and the lowest energy consumption. Its energy use was three orders of magnitude lower than two independent state of the art baseline computer vision systems. The dynamic power requirement for the complete system mapped to commercial off-the-shelf (COTS) hardware that includes a 5.6 Megapixel color camera processed by object detection and classification algorithms at 30 frames per second was measured at 21.7 Watts (W), for an effective energy consumption of 5.45 nanoJoules (nJ) per bit of incoming video. These unprecedented results show that the NEOVUS has the potential to revolutionize automated video object recognition toward enabling practical low-power and mobile video processing

  4. A neuromorphic system for video object recognition

    PubMed Central

    Khosla, Deepak; Chen, Yang; Kim, Kyungnam

    2014-01-01

    Automated video object recognition is a topic of emerging importance in both defense and civilian applications. This work describes an accurate and low-power neuromorphic architecture and system for real-time automated video object recognition. Our system, Neuormorphic Visual Understanding of Scenes (NEOVUS), is inspired by computational neuroscience models of feed-forward object detection and classification pipelines for processing visual data. The NEOVUS architecture is inspired by the ventral (what) and dorsal (where) streams of the mammalian visual pathway and integrates retinal processing, object detection based on form and motion modeling, and object classification based on convolutional neural networks. The object recognition performance and energy use of the NEOVUS was evaluated by the Defense Advanced Research Projects Agency (DARPA) under the Neovision2 program using three urban area video datasets collected from a mix of stationary and moving platforms. These datasets are challenging and include a large number of objects of different types in cluttered scenes, with varying illumination and occlusion conditions. In a systematic evaluation of five different teams by DARPA on these datasets, the NEOVUS demonstrated the best performance with high object recognition accuracy and the lowest energy consumption. Its energy use was three orders of magnitude lower than two independent state of the art baseline computer vision systems. The dynamic power requirement for the complete system mapped to commercial off-the-shelf (COTS) hardware that includes a 5.6 Megapixel color camera processed by object detection and classification algorithms at 30 frames per second was measured at 21.7 Watts (W), for an effective energy consumption of 5.45 nanoJoules (nJ) per bit of incoming video. These unprecedented results show that the NEOVUS has the potential to revolutionize automated video object recognition toward enabling practical low-power and mobile video processing

  5. A neuromorphic system for video object recognition.

    PubMed

    Khosla, Deepak; Chen, Yang; Kim, Kyungnam

    2014-01-01

    Automated video object recognition is a topic of emerging importance in both defense and civilian applications. This work describes an accurate and low-power neuromorphic architecture and system for real-time automated video object recognition. Our system, Neuormorphic Visual Understanding of Scenes (NEOVUS), is inspired by computational neuroscience models of feed-forward object detection and classification pipelines for processing visual data. The NEOVUS architecture is inspired by the ventral (what) and dorsal (where) streams of the mammalian visual pathway and integrates retinal processing, object detection based on form and motion modeling, and object classification based on convolutional neural networks. The object recognition performance and energy use of the NEOVUS was evaluated by the Defense Advanced Research Projects Agency (DARPA) under the Neovision2 program using three urban area video datasets collected from a mix of stationary and moving platforms. These datasets are challenging and include a large number of objects of different types in cluttered scenes, with varying illumination and occlusion conditions. In a systematic evaluation of five different teams by DARPA on these datasets, the NEOVUS demonstrated the best performance with high object recognition accuracy and the lowest energy consumption. Its energy use was three orders of magnitude lower than two independent state of the art baseline computer vision systems. The dynamic power requirement for the complete system mapped to commercial off-the-shelf (COTS) hardware that includes a 5.6 Megapixel color camera processed by object detection and classification algorithms at 30 frames per second was measured at 21.7 Watts (W), for an effective energy consumption of 5.45 nanoJoules (nJ) per bit of incoming video. These unprecedented results show that the NEOVUS has the potential to revolutionize automated video object recognition toward enabling practical low-power and mobile video processing

  6. Visual object recognition for mobile tourist information systems

    NASA Astrophysics Data System (ADS)

    Paletta, Lucas; Fritz, Gerald; Seifert, Christin; Luley, Patrick; Almer, Alexander

    2005-03-01

    We describe a mobile vision system that is capable of automated object identification using images captured from a PDA or a camera phone. We present a solution for the enabling technology of outdoors vision based object recognition that will extend state-of-the-art location and context aware services towards object based awareness in urban environments. In the proposed application scenario, tourist pedestrians are equipped with GPS, W-LAN and a camera attached to a PDA or a camera phone. They are interested whether their field of view contains tourist sights that would point to more detailed information. Multimedia type data about related history, the architecture, or other related cultural context of historic or artistic relevance might be explored by a mobile user who is intending to learn within the urban environment. Learning from ambient cues is in this way achieved by pointing the device towards the urban sight, capturing an image, and consequently getting information about the object on site and within the focus of attention, i.e., the users current field of view.

  7. Poka Yoke system based on image analysis and object recognition

    NASA Astrophysics Data System (ADS)

    Belu, N.; Ionescu, L. M.; Misztal, A.; Mazăre, A.

    2015-11-01

    Poka Yoke is a method of quality management which is related to prevent faults from arising during production processes. It deals with “fail-sating” or “mistake-proofing”. The Poka-yoke concept was generated and developed by Shigeo Shingo for the Toyota Production System. Poka Yoke is used in many fields, especially in monitoring production processes. In many cases, identifying faults in a production process involves a higher cost than necessary cost of disposal. Usually, poke yoke solutions are based on multiple sensors that identify some nonconformities. This means the presence of different equipment (mechanical, electronic) on production line. As a consequence, coupled with the fact that the method itself is an invasive, affecting the production process, would increase its price diagnostics. The bulky machines are the means by which a Poka Yoke system can be implemented become more sophisticated. In this paper we propose a solution for the Poka Yoke system based on image analysis and identification of faults. The solution consists of a module for image acquisition, mid-level processing and an object recognition module using associative memory (Hopfield network type). All are integrated into an embedded system with AD (Analog to Digital) converter and Zync 7000 (22 nm technology).

  8. Object detection by optical correlator and intelligence recognition surveillance systems

    NASA Astrophysics Data System (ADS)

    Sheng, Yunlong

    2013-09-01

    We report a recent work on robust object detection in high-resolution aerial imagery in urban environment for Intelligence, Surveillance and Recognition (ISR) missions. Our approaches used the simple linear iterative clustering (SLIC) algorithm, which combines regional and edge information to form the superpixels. The irregularity in size and shape of the superpixels measured with the Hausdorff distance served to determine the salient regions in the very large aerial images. Then, the car detection was performed with both the component-based approach and the featurebased approaches. We merged the superpixels with the statistical region merging (SRM) algorithm. The regions were described by the radiometric, geometrical moments and shape features, and classified using the Support Vector Machine (SVM). The cast shadow were detected and removed by a radiometry based tricolor attenuation model (TAM). Detection of object parts is less sensitive to occlusion, rotation, and changes in scale, view angle and illumination than detection of the object as whole. The object parts were combined to the object according to their unique spatial relations. On the other hand, we used the invariant scale invariant feature transform (SIFT) features to describe superpixels and classed them by the SVM as belong or not to the object. All along our recent work we still trace the brilliant ideas in early days by H. John Caulfield and other pioneers of optical pattern recognition, for improving the discrimination of the matched spatial filter with linear combinations of cross-correlations, which have been inherited transformed and reinvented to achieve tremendous progress.

  9. Vision-based object detection and recognition system for intelligent vehicles

    NASA Astrophysics Data System (ADS)

    Ran, Bin; Liu, Henry X.; Martono, Wilfung

    1999-01-01

    Recently, a proactive crash mitigation system is proposed to enhance the crash avoidance and survivability of the Intelligent Vehicles. Accurate object detection and recognition system is a prerequisite for a proactive crash mitigation system, as system component deployment algorithms rely on accurate hazard detection, recognition, and tracking information. In this paper, we present a vision-based approach to detect and recognize vehicles and traffic signs, obtain their information, and track multiple objects by using a sequence of color images taken from a moving vehicle. The entire system consist of two sub-systems, the vehicle detection and recognition sub-system and traffic sign detection and recognition sub-system. Both of the sub- systems consist of four models: object detection model, object recognition model, object information model, and object tracking model. In order to detect potential objects on the road, several features of the objects are investigated, which include symmetrical shape and aspect ratio of a vehicle and color and shape information of the signs. A two-layer neural network is trained to recognize different types of vehicles and a parameterized traffic sign model is established in the process of recognizing a sign. Tracking is accomplished by combining the analysis of single image frame with the analysis of consecutive image frames. The analysis of the single image frame is performed every ten full-size images. The information model will obtain the information related to the object, such as time to collision for the object vehicle and relative distance from the traffic sings. Experimental results demonstrated a robust and accurate system in real time object detection and recognition over thousands of image frames.

  10. Toward a unified model of face and object recognition in the human visual system

    PubMed Central

    Wallis, Guy

    2013-01-01

    Our understanding of the mechanisms and neural substrates underlying visual recognition has made considerable progress over the past 30 years. During this period, accumulating evidence has led many scientists to conclude that objects and faces are recognised in fundamentally distinct ways, and in fundamentally distinct cortical areas. In the psychological literature, in particular, this dissociation has led to a palpable disconnect between theories of how we process and represent the two classes of object. This paper follows a trend in part of the recognition literature to try to reconcile what we know about these two forms of recognition by considering the effects of learning. Taking a widely accepted, self-organizing model of object recognition, this paper explains how such a system is affected by repeated exposure to specific stimulus classes. In so doing, it explains how many aspects of recognition generally regarded as unusual to faces (holistic processing, configural processing, sensitivity to inversion, the other-race effect, the prototype effect, etc.) are emergent properties of category-specific learning within such a system. Overall, the paper describes how a single model of recognition learning can and does produce the seemingly very different types of representation associated with faces and objects. PMID:23966963

  11. Toward a unified model of face and object recognition in the human visual system.

    PubMed

    Wallis, Guy

    2013-01-01

    Our understanding of the mechanisms and neural substrates underlying visual recognition has made considerable progress over the past 30 years. During this period, accumulating evidence has led many scientists to conclude that objects and faces are recognised in fundamentally distinct ways, and in fundamentally distinct cortical areas. In the psychological literature, in particular, this dissociation has led to a palpable disconnect between theories of how we process and represent the two classes of object. This paper follows a trend in part of the recognition literature to try to reconcile what we know about these two forms of recognition by considering the effects of learning. Taking a widely accepted, self-organizing model of object recognition, this paper explains how such a system is affected by repeated exposure to specific stimulus classes. In so doing, it explains how many aspects of recognition generally regarded as unusual to faces (holistic processing, configural processing, sensitivity to inversion, the other-race effect, the prototype effect, etc.) are emergent properties of category-specific learning within such a system. Overall, the paper describes how a single model of recognition learning can and does produce the seemingly very different types of representation associated with faces and objects. PMID:23966963

  12. Classification of fragments of objects by the Fourier masks pattern recognition system

    NASA Astrophysics Data System (ADS)

    Barajas-García, Carolina; Solorza-Calderón, Selene; Álvarez-Borrego, Josué

    2016-05-01

    The automation process of the pattern recognition for fragments of objects is a challenge to humanity. For humans it is relatively easy to classify the fragment of some object even if it is isolated and perhaps this identification could be more complicated if it is partially overlapped by other object. However, the emulation of the functions of the human eye and brain by a computer is not a trivial issue. This paper presents a pattern recognition digital system based on Fourier binary rings masks in order to classify fragments of objects. The system is invariant to position, scale and rotation, and it is robust in the classification of images that have noise. Moreover, it classifies images that present an occlusion or elimination of approximately 50% of the area of the object.

  13. Object Recognition System-on-Chip Using the Support Vector Machines

    NASA Astrophysics Data System (ADS)

    Reyna-Rojas, Roberto; Houzet, Dominique; Dragomirescu, Daniela; Carlier, Florent; Ouadjaout, Salim

    2005-12-01

    The first aim of this work is to propose the design of a system-on-chip (SoC) platform dedicated to digital image and signal processing, which is tuned to implement efficiently multiply-and-accumulate (MAC) vector/matrix operations. The second aim of this work is to implement a recent promising neural network method, namely, the support vector machine (SVM) used for real-time object recognition, in order to build a vision machine. With such a reconfigurable and programmable SoC platform, it is possible to implement any SVM function dedicated to any object recognition problem. The final aim is to obtain an automatic reconfiguration of the SoC platform, based on the results of the learning phase on an objects' database, which makes it possible to recognize practically any object without manual programming. Recognition can be of any kind that is from image to signal data. Such a system is a general-purpose automatic classifier. Many applications can be considered as a classification problem, but are usually treated specifically in order to optimize the cost of the implemented solution. The cost of our approach is more important than a dedicated one, but in a near future, hundreds of millions of gates will be common and affordable compared to the design cost. What we are proposing here is a general-purpose classification neural network implemented on a reconfigurable SoC platform. The first version presented here is limited in size and thus in object recognition performances, but can be easily upgraded according to technology improvements.

  14. A knowledge-based object recognition system for applications in the space station

    NASA Technical Reports Server (NTRS)

    Dhawan, Atam P.

    1988-01-01

    A knowledge-based three-dimensional (3D) object recognition system is being developed. The system uses primitive-based hierarchical relational and structural matching for the recognition of 3D objects in the two-dimensional (2D) image for interpretation of the 3D scene. At present, the pre-processing, low-level preliminary segmentation, rule-based segmentation, and the feature extraction are completed. The data structure of the primitive viewing knowledge-base (PVKB) is also completed. Algorithms and programs based on attribute-trees matching for decomposing the segmented data into valid primitives were developed. The frame-based structural and relational descriptions of some objects were created and stored in a knowledge-base. This knowledge-base of the frame-based descriptions were developed on the MICROVAX-AI microcomputer in LISP environment. The simulated 3D scene of simple non-overlapping objects as well as real camera data of images of 3D objects of low-complexity have been successfully interpreted.

  15. An Intelligent Systems Approach to Automated Object Recognition: A Preliminary Study

    USGS Publications Warehouse

    Maddox, Brian G.; Swadley, Casey L.

    2002-01-01

    Attempts at fully automated object recognition systems have met with varying levels of success over the years. However, none of the systems have achieved high enough accuracy rates to be run unattended. One of the reasons for this may be that they are designed from the computer's point of view and rely mainly on image-processing methods. A better solution to this problem may be to make use of modern advances in computational intelligence and distributed processing to try to mimic how the human brain is thought to recognize objects. As humans combine cognitive processes with detection techniques, such a system would combine traditional image-processing techniques with computer-based intelligence to determine the identity of various objects in a scene.

  16. Geometric filtration of classification-based object detectors in realtime road scene recognition systems

    NASA Astrophysics Data System (ADS)

    Prun, Viktor; Bocharov, Dmitry; Koptelov, Ivan; Sholomov, Dmitry; Postnikov, Vassily

    2015-12-01

    We study the issue of performance improvement of classification-based object detectors by including certain geometric-oriented filters. Configurations of the observed 3D scene may be used as a priori or a posteriori information for object filtration. A priori information is used to select only those object parameters (size and position on image plane) that are in accordance with the scene, restricting implausible combinations of parameters. On the other hand the detection robustness can be enhanced by rejecting detection results using a posteriori information about 3D scene. For example, relative location of detected objects can be used as criteria for filtration. We have included proposed filters in object detection modules of two different industrial vision-based recognition systems and compared the resulting detection quality before detectors improving and after. Filtering with a priori information leads to significant decrease of detector's running time per frame and increase of number of correctly detected objects. Including filter based on a posteriori information leads to decrease of object detection false positive rate.

  17. Object recognition by active fusion

    NASA Astrophysics Data System (ADS)

    Prantl, Manfred; Kopp-Borotschnig, Hermann; Ganster, Harald; Sinclair, David; Pinz, Axel J.

    1996-10-01

    Today's computer vision applications often have to deal with multiple, uncertain, and incomplete visual information. In this paper, we apply a new method, termed 'active fusion', to the problem of generic object recognition. Active fusion provides a common framework for active selection and combination of information from multiple sources in order to arrive at a reliable result at reasonable costs. In our experimental setup we use a camera mounted on a 2m by 1.5m x/z-table observing objects placed on a rotating table. Zoom, pan, tilt, and aperture setting of the camera can be controlled by the system. We follow a part-based approach, trying to decompose objects into parts, which are modeled as geons. The active fusion system starts from an initial view of the objects placed on the table and is continuously trying to refine its current object hypotheses by requesting additional views. The implementation of active fusion on the basis of probability theory, Dempster-Shafer's theory of evidence and fuzzy set theory is discussed. First results demonstrating segmentation improvements by active fusion are presented.

  18. Recurrent Processing during Object Recognition

    PubMed Central

    O’Reilly, Randall C.; Wyatte, Dean; Herd, Seth; Mingus, Brian; Jilk, David J.

    2013-01-01

    How does the brain learn to recognize objects visually, and perform this difficult feat robustly in the face of many sources of ambiguity and variability? We present a computational model based on the biology of the relevant visual pathways that learns to reliably recognize 100 different object categories in the face of naturally occurring variability in location, rotation, size, and lighting. The model exhibits robustness to highly ambiguous, partially occluded inputs. Both the unified, biologically plausible learning mechanism and the robustness to occlusion derive from the role that recurrent connectivity and recurrent processing mechanisms play in the model. Furthermore, this interaction of recurrent connectivity and learning predicts that high-level visual representations should be shaped by error signals from nearby, associated brain areas over the course of visual learning. Consistent with this prediction, we show how semantic knowledge about object categories changes the nature of their learned visual representations, as well as how this representational shift supports the mapping between perceptual and conceptual knowledge. Altogether, these findings support the potential importance of ongoing recurrent processing throughout the brain’s visual system and suggest ways in which object recognition can be understood in terms of interactions within and between processes over time. PMID:23554596

  19. Acoustic signature recognition technique for Human-Object Interactions (HOI) in persistent surveillance systems

    NASA Astrophysics Data System (ADS)

    Alkilani, Amjad; Shirkhodaie, Amir

    2013-05-01

    Handling, manipulation, and placement of objects, hereon called Human-Object Interaction (HOI), in the environment generate sounds. Such sounds are readily identifiable by the human hearing. However, in the presence of background environment noises, recognition of minute HOI sounds is challenging, though vital for improvement of multi-modality sensor data fusion in Persistent Surveillance Systems (PSS). Identification of HOI sound signatures can be used as precursors to detection of pertinent threats that otherwise other sensor modalities may miss to detect. In this paper, we present a robust method for detection and classification of HOI events via clustering of extracted features from training of HOI acoustic sound waves. In this approach, salient sound events are preliminary identified and segmented from background via a sound energy tracking method. Upon this segmentation, frequency spectral pattern of each sound event is modeled and its features are extracted to form a feature vector for training. To reduce dimensionality of training feature space, a Principal Component Analysis (PCA) technique is employed to expedite fast classification of test feature vectors, a kd-tree and Random Forest classifiers are trained for rapid classification of training sound waves. Each classifiers employs different similarity distance matching technique for classification. Performance evaluations of classifiers are compared for classification of a batch of training HOI acoustic signatures. Furthermore, to facilitate semantic annotation of acoustic sound events, a scheme based on Transducer Mockup Language (TML) is proposed. The results demonstrate the proposed approach is both reliable and effective, and can be extended to future PSS applications.

  20. Recognition of movement object collision

    NASA Astrophysics Data System (ADS)

    Chang, Hsiao Tsu; Sun, Geng-tian; Zhang, Yan

    1991-03-01

    The paper explores the collision recognition of two objects in both crisscross and revolution motions A mathematical model has been established based on the continuation theory. The objects of any shape may be regarded as being built of many 3siniplexes or their convex hulls. Therefore the collision problem of two object in motion can be reduced to the collision of two corresponding 3siinplexes on two respective objects accordingly. Thus an optimized algorithm is developed for collision avoidance which is suitable for computer control and eliminating the need for vision aid. With this algorithm computation time has been reduced significantly. This algorithm is applicable to the path planning of mobile robots And also is applicable to collision avoidance of the anthropomorphic arms grasping two complicated shaped objects. The algorithm is realized using LISP language on a VAX8350 minicomputer.

  1. Visual object recognition and tracking

    NASA Technical Reports Server (NTRS)

    Chang, Chu-Yin (Inventor); English, James D. (Inventor); Tardella, Neil M. (Inventor)

    2010-01-01

    This invention describes a method for identifying and tracking an object from two-dimensional data pictorially representing said object by an object-tracking system through processing said two-dimensional data using at least one tracker-identifier belonging to the object-tracking system for providing an output signal containing: a) a type of the object, and/or b) a position or an orientation of the object in three-dimensions, and/or c) an articulation or a shape change of said object in said three dimensions.

  2. A fast 3-D object recognition algorithm for the vision system of a special-purpose dexterous manipulator

    NASA Technical Reports Server (NTRS)

    Hung, Stephen H. Y.

    1989-01-01

    A fast 3-D object recognition algorithm that can be used as a quick-look subsystem to the vision system for the Special-Purpose Dexterous Manipulator (SPDM) is described. Global features that can be easily computed from range data are used to characterize the images of a viewer-centered model of an object. This algorithm will speed up the processing by eliminating the low level processing whenever possible. It may identify the object, reject a set of bad data in the early stage, or create a better environment for a more powerful algorithm to carry the work further.

  3. Pyroelectric linear array sensor for object recognition

    NASA Astrophysics Data System (ADS)

    Chari, Srikant; Jacobs, Eddie L.; Choudhary, Divya

    2014-02-01

    This paper presents a proof of concept sensor system based on a linear array of pyroelectric detectors for recognition of moving objects. The utility of this prototype sensor is demonstrated by its use in trail monitoring and perimeter protection applications for classifying humans against animals with object motion transverse to the field of view of the sensor array. Data acquisition using the system was performed under varied terrains and using a wide variety of animals and humans. With the objective of eventually porting the algorithms onto a low resource computational platform, simple signal processing, feature extraction, and classification techniques are used. The object recognition algorithm uses a combination of geometrical and texture features to provide limited insensitivity to range and speed. Analysis of system performance shows its effectiveness in discriminating humans and animals with high classification accuracy.

  4. Geometric hashing and object recognition

    NASA Astrophysics Data System (ADS)

    Stiller, Peter F.; Huber, Birkett

    1999-09-01

    We discuss a new geometric hashing method for searching large databases of 2D images (or 3D objects) to match a query built from geometric information presented by a single 3D object (or single 2D image). The goal is to rapidly determine a small subset of the images that potentially contain a view of the given object (or a small set of objects that potentially match the item in the image). Since this must be accomplished independent of the pose of the object, the objects and images, which are characterized by configurations of geometric features such as points, lines and/or conics, must be treated using a viewpoint invariant formulation. We are therefore forced to characterize these configurations in terms of their 3D and 2D geometric invariants. The crucial relationship between the 3D geometry and its 'residual' in 2D is expressible as a correspondence (in the sense of algebraic geometry). Computing a set of generating equations for the ideal of this correspondence gives a complete characterization of the view of independent relationships between an object and all of its possible images. Once a set of generators is in hand, it can be used to devise efficient recognition algorithms and to give an efficient geometric hashing scheme. This requires exploiting the form and symmetry of the equations. The result is a multidimensional access scheme whose efficiency we examine. Several potential directions for improving this scheme are also discussed. Finally, in a brief appendix, we discuss an alternative approach to invariants for generalized perspective that replaces the standard invariants by a subvariety of a Grassmannian. The advantage of this is that one can circumvent many annoying general position assumptions and arrive at invariant equations (in the Plucker coordinates) that are more numerically robust in applications.

  5. Object recognition by artificial cortical maps.

    PubMed

    Plebe, Alessio; Domenella, Rosaria Grazia

    2007-09-01

    Object recognition is one of the most important functions of the human visual system, yet one of the least understood, this despite the fact that vision is certainly the most studied function of the brain. We understand relatively well how several processes in the cortical visual areas that support recognition capabilities take place, such as orientation discrimination and color constancy. This paper proposes a model of the development of object recognition capability, based on two main theoretical principles. The first is that recognition does not imply any sort of geometrical reconstruction, it is instead fully driven by the two dimensional view captured by the retina. The second assumption is that all the processing functions involved in recognition are not genetically determined or hardwired in neural circuits, but are the result of interactions between epigenetic influences and basic neural plasticity mechanisms. The model is organized in modules roughly related to the main visual biological areas, and is implemented mainly using the LISSOM architecture, a recent neural self-organizing map model that simulates the effects of intercortical lateral connections. This paper shows how recognition capabilities, similar to those found in brain ventral visual areas, can develop spontaneously by exposure to natural images in an artificial cortical model.

  6. Statistical Model For Pseudo-Moving Objects Recognition In Video Surveillance Systems

    NASA Astrophysics Data System (ADS)

    Vishnyakov, B.; Egorov, A.; Sidyakin, S.; Malin, I.; Vizilter, Y.

    2014-08-01

    This paper considers a statistical approach to define pseudo-moving (false) objects in video surveillance systems by constructing systems of hypothesis with the criteria based on statistical behavioral particularities. The obtained results are integrated in two ways: using the Bayes' theorem or the logistic regression. FAR-FRR curves are plotted for each system of hypothesis and also for the decision rule. The results of the proposed methods are obtained on test video databases.

  7. Relations among Early Object Recognition Skills: Objects and Letters

    ERIC Educational Resources Information Center

    Augustine, Elaine; Jones, Susan S.; Smith, Linda B.; Longfield, Erica

    2015-01-01

    Human visual object recognition is multifaceted and comprised of several domains of expertise. Developmental relations between young children's letter recognition and their 3-dimensional object recognition abilities are implicated on several grounds but have received little research attention. Here, we ask how preschoolers' success in recognizing…

  8. Infant Visual Attention and Object Recognition

    PubMed Central

    Reynolds, Greg D.

    2015-01-01

    This paper explores the role visual attention plays in the recognition of objects in infancy. Research and theory on the development of infant attention and recognition memory are reviewed in three major sections. The first section reviews some of the major findings and theory emerging from a rich tradition of behavioral research utilizing preferential looking tasks to examine visual attention and recognition memory in infancy. The second section examines research utilizing neural measures of attention and object recognition in infancy as well as research on brain-behavior relations in the early development of attention and recognition memory. The third section addresses potential areas of the brain involved in infant object recognition and visual attention. An integrated synthesis of some of the existing models of the development of visual attention is presented which may account for the observed changes in behavioral and neural measures of visual attention and object recognition that occur across infancy. PMID:25596333

  9. Relations among early object recognition skills: Objects and letters

    PubMed Central

    Augustine, Elaine; Jones, Susan S.; Smith, Linda B.; Longfield, Erica

    2014-01-01

    Human visual object recognition is multifaceted, with several domains of expertise. Developmental relations between young children's letter recognition and their 3-dimensional object recognition abilities are implicated on several grounds but have received little research attention. Here, we ask how preschoolers’ success in recognizing letters relates to their ability to recognize 3-dimensional objects from sparse shape information alone. A relation is predicted because perception of the spatial relations is critical in both domains. Seventy-three 2 ½- to 4-year-old children completed a Letter Recognition task, measuring the ability to identify a named letter among 3 letters with similar shapes, and a “Shape Caricature Recognition” task, measuring recognition of familiar objects from sparse, abstract information about their part shapes and the spatial relations among those parts. Children also completed a control “Shape Bias” task, in which success depends on recognition of overall object shape but not of relational structure. Children's success in letter recognition was positively related to their shape caricature recognition scores, but not to their shape bias scores. The results suggest that letter recognition builds upon developing skills in attending to and representing the relational structure of object shape, and that these skills are common to both 2-dimensional and 3-dimensional object perception. PMID:25969673

  10. From brain synapses to systems for learning and memory: Object recognition, spatial navigation, timed conditioning, and movement control.

    PubMed

    Grossberg, Stephen

    2015-09-24

    This article provides an overview of neural models of synaptic learning and memory whose expression in adaptive behavior depends critically on the circuits and systems in which the synapses are embedded. It reviews Adaptive Resonance Theory, or ART, models that use excitatory matching and match-based learning to achieve fast category learning and whose learned memories are dynamically stabilized by top-down expectations, attentional focusing, and memory search. ART clarifies mechanistic relationships between consciousness, learning, expectation, attention, resonance, and synchrony. ART models are embedded in ARTSCAN architectures that unify processes of invariant object category learning, recognition, spatial and object attention, predictive remapping, and eye movement search, and that clarify how conscious object vision and recognition may fail during perceptual crowding and parietal neglect. The generality of learned categories depends upon a vigilance process that is regulated by acetylcholine via the nucleus basalis. Vigilance can get stuck at too high or too low values, thereby causing learning problems in autism and medial temporal amnesia. Similar synaptic learning laws support qualitatively different behaviors: Invariant object category learning in the inferotemporal cortex; learning of grid cells and place cells in the entorhinal and hippocampal cortices during spatial navigation; and learning of time cells in the entorhinal-hippocampal system during adaptively timed conditioning, including trace conditioning. Spatial and temporal processes through the medial and lateral entorhinal-hippocampal system seem to be carried out with homologous circuit designs. Variations of a shared laminar neocortical circuit design have modeled 3D vision, speech perception, and cognitive working memory and learning. A complementary kind of inhibitory matching and mismatch learning controls movement. This article is part of a Special Issue entitled SI: Brain and Memory. PMID

  11. From brain synapses to systems for learning and memory: Object recognition, spatial navigation, timed conditioning, and movement control.

    PubMed

    Grossberg, Stephen

    2015-09-24

    This article provides an overview of neural models of synaptic learning and memory whose expression in adaptive behavior depends critically on the circuits and systems in which the synapses are embedded. It reviews Adaptive Resonance Theory, or ART, models that use excitatory matching and match-based learning to achieve fast category learning and whose learned memories are dynamically stabilized by top-down expectations, attentional focusing, and memory search. ART clarifies mechanistic relationships between consciousness, learning, expectation, attention, resonance, and synchrony. ART models are embedded in ARTSCAN architectures that unify processes of invariant object category learning, recognition, spatial and object attention, predictive remapping, and eye movement search, and that clarify how conscious object vision and recognition may fail during perceptual crowding and parietal neglect. The generality of learned categories depends upon a vigilance process that is regulated by acetylcholine via the nucleus basalis. Vigilance can get stuck at too high or too low values, thereby causing learning problems in autism and medial temporal amnesia. Similar synaptic learning laws support qualitatively different behaviors: Invariant object category learning in the inferotemporal cortex; learning of grid cells and place cells in the entorhinal and hippocampal cortices during spatial navigation; and learning of time cells in the entorhinal-hippocampal system during adaptively timed conditioning, including trace conditioning. Spatial and temporal processes through the medial and lateral entorhinal-hippocampal system seem to be carried out with homologous circuit designs. Variations of a shared laminar neocortical circuit design have modeled 3D vision, speech perception, and cognitive working memory and learning. A complementary kind of inhibitory matching and mismatch learning controls movement. This article is part of a Special Issue entitled SI: Brain and Memory.

  12. A Vision System for Real Time Road and Object Recognition for Vehicle Guidance

    NASA Astrophysics Data System (ADS)

    Jackson, T. A.; Samuelsen, G. S.

    1987-02-01

    One crucial component of a control system for autonomous vehicle guidance is real time image analysis. This system part is burdened by the maximum flow of information. To overcome the high demands in computation power a combination of knowledge based scene analysis and special hardware has been developed. The use of knowledge based image analysis supports real time processing not by schematically evaluating all parts of the image, but only evaluating those which contain relevant information. This is due to the fact that in many practical problems the relevant information is very unevenly distributed over the image. Preknowledge of the problem or the aim of the mission and expectations or predictions about the scene sustantially reduce the amount of information to be processed. The operations during such an analysis may be divided into two classes - simple processes, e.g. filters, correlation, contour processing and simple search strategies - complex search and control strategy This classification supplied the concept for a special hardware. The complex tasks are performed by a universal processor 80286 while the remaining tasks are executed by a special coprocessor (including image memory). This combination permits the use of filter masks with a arbitrary geometry together with a powerful search strategy. A number of these basic modules may be configured into a multiprocessor system. The universal processor is programmed in a high level language. To support the coprocessor a set of software tools has been built. They permit interactive graphical manipulation of filtermasks, generation of simple search strategies and non real time simulation. Also the real data structures that control the function of the coprocessor are generated by this software package. The system is used within our autonomous vehicle project. One set of algorithms tracks the border lines of the road even if they are broken or disturbed by dirt. Also shadows of bridges crossing the road are

  13. Changes to the object recognition system in patients with dementia of the Alzheimer's type.

    PubMed

    Purdy, K S; McMullen, P A; Freedman, M

    2002-07-01

    Do DAT patients show category-specific deficits in object identification, and do they arise from semantic or visual damage? Participants decided whether line drawings of living and nonliving objects matched names at superordinate, basic, or subordinate levels. Patients were most impaired with superordinate decisions. Controls had most difficulty with subordinate decisions. No category-specific deficit was found with patients. Impaired superordinate decisions by the patients support semantic damage. If category-specific deficits arise from damaged semantics, they should have been found. Since they were not, and since patients performed subordinate decisions the best, a visual basis to category specificity is supported. Finally, a living advantage was found with normal observers which cannot be spurious due to differences in concept familiarity since living and nonliving objects were matched for this variable. PMID:15259393

  14. Object and event recognition for stroke rehabilitation

    NASA Astrophysics Data System (ADS)

    Ghali, Ahmed; Cunningham, Andrew S.; Pridmore, Tony P.

    2003-06-01

    Stroke is a major cause of disability and health care expenditure around the world. Existing stroke rehabilitation methods can be effective but are costly and need to be improved. Even modest improvements in the effectiveness of rehabilitation techniques could produce large benefits in terms of quality of life. The work reported here is part of an ongoing effort to integrate virtual reality and machine vision technologies to produce innovative stroke rehabilitation methods. We describe a combined object recognition and event detection system that provides real time feedback to stroke patients performing everyday kitchen tasks necessary for independent living, e.g. making a cup of coffee. The image plane position of each object, including the patient"s hand, is monitored using histogram-based recognition methods. The relative positions of hand and objects are then reported to a task monitor that compares the patient"s actions against a model of the target task. A prototype system has been constructed and is currently undergoing technical and clinical evaluation.

  15. Object Recognition Memory and the Rodent Hippocampus

    ERIC Educational Resources Information Center

    Broadbent, Nicola J.; Gaskin, Stephane; Squire, Larry R.; Clark, Robert E.

    2010-01-01

    In rodents, the novel object recognition task (NOR) has become a benchmark task for assessing recognition memory. Yet, despite its widespread use, a consensus has not developed about which brain structures are important for task performance. We assessed both the anterograde and retrograde effects of hippocampal lesions on performance in the NOR…

  16. The Role of Object Recognition in Young Infants' Object Segregation.

    ERIC Educational Resources Information Center

    Carey, Susan; Williams, Travis

    2001-01-01

    Discusses Needham's findings by asserting that they extend understanding of infant perception by showing that the memory representations infants draw upon have bound together information about shape, color, and pattern. Considers the distinction between two senses of "recognition" and asks in which sense object recognition contributes to object…

  17. Neural-Network Object-Recognition Program

    NASA Technical Reports Server (NTRS)

    Spirkovska, L.; Reid, M. B.

    1993-01-01

    HONTIOR computer program implements third-order neural network exhibiting invariance under translation, change of scale, and in-plane rotation. Invariance incorporated directly into architecture of network. Only one view of each object needed to train network for two-dimensional-translation-invariant recognition of object. Also used for three-dimensional-transformation-invariant recognition by training network on only set of out-of-plane rotated views. Written in C language.

  18. Developing a multi-Kinect-system for monitoring in dairy cows: object recognition and surface analysis using wavelets.

    PubMed

    Salau, J; Haas, J H; Thaller, G; Leisen, M; Junge, W

    2016-09-01

    Camera-based systems in dairy cattle were intensively studied over the last years. Different from this study, single camera systems with a limited range of applications were presented, mostly using 2D cameras. This study presents current steps in the development of a camera system comprising multiple 3D cameras (six Microsoft Kinect cameras) for monitoring purposes in dairy cows. An early prototype was constructed, and alpha versions of software for recording, synchronizing, sorting and segmenting images and transforming the 3D data in a joint coordinate system have already been implemented. This study introduced the application of two-dimensional wavelet transforms as method for object recognition and surface analyses. The method was explained in detail, and four differently shaped wavelets were tested with respect to their reconstruction error concerning Kinect recorded depth maps from different camera positions. The images' high frequency parts reconstructed from wavelet decompositions using the haar and the biorthogonal 1.5 wavelet were statistically analyzed with regard to the effects of image fore- or background and of cows' or persons' surface. Furthermore, binary classifiers based on the local high frequencies have been implemented to decide whether a pixel belongs to the image foreground and if it was located on a cow or a person. Classifiers distinguishing between image regions showed high (⩾0.8) values of Area Under reciever operation characteristic Curve (AUC). The classifications due to species showed maximal AUC values of 0.69. PMID:26837672

  19. Object recognition memory and the rodent hippocampus.

    PubMed

    Broadbent, Nicola J; Gaskin, Stephane; Squire, Larry R; Clark, Robert E

    2010-01-01

    In rodents, the novel object recognition task (NOR) has become a benchmark task for assessing recognition memory. Yet, despite its widespread use, a consensus has not developed about which brain structures are important for task performance. We assessed both the anterograde and retrograde effects of hippocampal lesions on performance in the NOR task. Rats received 12 5-min exposures to two identical objects and then received either bilateral lesions of the hippocampus or sham surgery 1 d, 4 wk, or 8 wk after the final exposure. On a retention test 2 wk after surgery, the 1-d and 4-wk hippocampal lesion groups exhibited impaired object recognition memory. In contrast, the 8-wk hippocampal lesion group performed similarly to controls, and both groups exhibited a preference for the novel object. These same rats were then given four postoperative tests using unique object pairs and a 3-h delay between the exposure phase and the test phase. Hippocampal lesions produced moderate and reliable memory impairment. The results suggest that the hippocampus is important for object recognition memory.

  20. Integration trumps selection in object recognition.

    PubMed

    Saarela, Toni P; Landy, Michael S

    2015-03-30

    Finding and recognizing objects is a fundamental task of vision. Objects can be defined by several "cues" (color, luminance, texture, etc.), and humans can integrate sensory cues to improve detection and recognition [1-3]. Cortical mechanisms fuse information from multiple cues [4], and shape-selective neural mechanisms can display cue invariance by responding to a given shape independent of the visual cue defining it [5-8]. Selective attention, in contrast, improves recognition by isolating a subset of the visual information [9]. Humans can select single features (red or vertical) within a perceptual dimension (color or orientation), giving faster and more accurate responses to items having the attended feature [10, 11]. Attention elevates neural responses and sharpens neural tuning to the attended feature, as shown by studies in psychophysics and modeling [11, 12], imaging [13-16], and single-cell and neural population recordings [17, 18]. Besides single features, attention can select whole objects [19-21]. Objects are among the suggested "units" of attention because attention to a single feature of an object causes the selection of all of its features [19-21]. Here, we pit integration against attentional selection in object recognition. We find, first, that humans can integrate information near optimally from several perceptual dimensions (color, texture, luminance) to improve recognition. They cannot, however, isolate a single dimension even when the other dimensions provide task-irrelevant, potentially conflicting information. For object recognition, it appears that there is mandatory integration of information from multiple dimensions of visual experience. The advantage afforded by this integration, however, comes at the expense of attentional selection. PMID:25802154

  1. Integration trumps selection in object recognition

    PubMed Central

    Saarela, Toni P.; Landy, Michael S.

    2015-01-01

    Summary Finding and recognizing objects is a fundamental task of vision. Objects can be defined by several “cues” (color, luminance, texture etc.), and humans can integrate sensory cues to improve detection and recognition [1–3]. Cortical mechanisms fuse information from multiple cues [4], and shape-selective neural mechanisms can display cue-invariance by responding to a given shape independent of the visual cue defining it [5–8]. Selective attention, in contrast, improves recognition by isolating a subset of the visual information [9]. Humans can select single features (red or vertical) within a perceptual dimension (color or orientation), giving faster and more accurate responses to items having the attended feature [10,11]. Attention elevates neural responses and sharpens neural tuning to the attended feature, as shown by studies in psychophysics and modeling [11,12], imaging [13–16], and single-cell and neural population recordings [17,18]. Besides single features, attention can select whole objects [19–21]. Objects are among the suggested “units” of attention because attention to a single feature of an object causes the selection of all of its features [19–21]. Here, we pit integration against attentional selection in object recognition. We find, first, that humans can integrate information near-optimally from several perceptual dimensions (color, texture, luminance) to improve recognition. They cannot, however, isolate a single dimension even when the other dimensions provide task-irrelevant, potentially conflicting information. For object recognition, it appears that there is mandatory integration of information from multiple dimensions of visual experience. The advantage afforded by this integration, however, comes at the expense of attentional selection. PMID:25802154

  2. Examining object location and object recognition memory in mice.

    PubMed

    Vogel-Ciernia, Annie; Wood, Marcelo A

    2014-10-08

    This unit is designed to provide sufficient instruction for the setup and execution of tests for object location and object recognition in adult mice. This task is ideally suited for the study of a variety of mouse models that examine disease mechanisms and novel therapeutic targets. By altering several key parameters, the experimenter can investigate short-term or long-term memory and look for either memory impairments or enhancements. Object location and object recognition memory tasks rely on a rodent's innate preference for novelty, and can be conducted sequentially in the same cohort of animals. These two tasks avoid the inherent stress induced with other common measures of rodent memory such as fear conditioning and the Morris water maze. This protocol covers detailed instructions on conducting both tasks, as well as key points concerning data collection, analysis, and interpretation.

  3. Divergent short- and long-term effects of acute stress in object recognition memory are mediated by endogenous opioid system activation.

    PubMed

    Nava-Mesa, Mauricio O; Lamprea, Marisol R; Múnera, Alejandro

    2013-11-01

    Acute stress induces short-term object recognition memory impairment and elicits endogenous opioid system activation. The aim of this study was thus to evaluate whether opiate system activation mediates the acute stress-induced object recognition memory changes. Adult male Wistar rats were trained in an object recognition task designed to test both short- and long-term memory. Subjects were randomly assigned to receive an intraperitoneal injection of saline, 1 mg/kg naltrexone or 3 mg/kg naltrexone, four and a half hours before the sample trial. Five minutes after the injection, half the subjects were submitted to movement restraint during four hours while the other half remained in their home cages. Non-stressed subjects receiving saline (control) performed adequately during the short-term memory test, while stressed subjects receiving saline displayed impaired performance. Naltrexone prevented such deleterious effect, in spite of the fact that it had no intrinsic effect on short-term object recognition memory. Stressed subjects receiving saline and non-stressed subjects receiving naltrexone performed adequately during the long-term memory test; however, control subjects as well as stressed subjects receiving a high dose of naltrexone performed poorly. Control subjects' dissociated performance during both memory tests suggests that the short-term memory test induced a retroactive interference effect mediated through light opioid system activation; such effect was prevented either by low dose naltrexone administration or by strongly activating the opioid system through acute stress. Both short-term memory retrieval impairment and long-term memory improvement observed in stressed subjects may have been mediated through strong opioid system activation, since they were prevented by high dose naltrexone administration. Therefore, the activation of the opioid system plays a dual modulating role in object recognition memory.

  4. Integrating task-directed planning with reactive object recognition

    NASA Astrophysics Data System (ADS)

    Dickinson, Sven J.; Stevenson, Suzanne; Amdur, Eugene; Tsotsos, John K.; Olsson, Lars

    1993-08-01

    We describe a robot vision system that achieves complex object recognition with two layers of behaviors, performing the tasks of planning and object recognition, respectively. The recognition layer is a pipeline in which successive stages take in images from a stereo head, recover relevant features, build intermediate representations, and deposit 3-D objects into a world model. Each stage is an independent process that reacts automatically to output from the previous stage. This reactive system operates continuously and autonomously to construct the robot's 3-D model of the environment. Sitting above the recognition pipeline is the planner which is responsible for populating the world model with objects that satisfy the high-level goals of the system. For example, upon examination of the world model, the planner can decide to direct the head to another location, gating new images into the recognition pipeline, causing new objects to be deposited into the world model. Alternatively, the planner can alter the recognition behavior of the pipeline so that objects of a certain type or at a certain location appear in the world model.

  5. Parallel and distributed computation for fault-tolerant object recognition

    NASA Technical Reports Server (NTRS)

    Wechsler, Harry

    1988-01-01

    The distributed associative memory (DAM) model is suggested for distributed and fault-tolerant computation as it relates to object recognition tasks. The fault-tolerance is with respect to geometrical distortions (scale and rotation), noisy inputs, occulsion/overlap, and memory faults. An experimental system was developed for fault-tolerant structure recognition which shows the feasibility of such an approach. The approach is futher extended to the problem of multisensory data integration and applied successfully to the recognition of colored polyhedral objects.

  6. Shape and Color Features for Object Recognition Search

    NASA Technical Reports Server (NTRS)

    Duong, Tuan A.; Duong, Vu A.; Stubberud, Allen R.

    2012-01-01

    A bio-inspired shape feature of an object of interest emulates the integration of the saccadic eye movement and horizontal layer in vertebrate retina for object recognition search where a single object can be used one at a time. The optimal computational model for shape-extraction-based principal component analysis (PCA) was also developed to reduce processing time and enable the real-time adaptive system capability. A color feature of the object is employed as color segmentation to empower the shape feature recognition to solve the object recognition in the heterogeneous environment where a single technique - shape or color - may expose its difficulties. To enable the effective system, an adaptive architecture and autonomous mechanism were developed to recognize and adapt the shape and color feature of the moving object. The bio-inspired object recognition based on bio-inspired shape and color can be effective to recognize a person of interest in the heterogeneous environment where the single technique exposed its difficulties to perform effective recognition. Moreover, this work also demonstrates the mechanism and architecture of the autonomous adaptive system to enable the realistic system for the practical use in the future.

  7. Object recognition with hierarchical discriminant saliency networks

    PubMed Central

    Han, Sunhyoung; Vasconcelos, Nuno

    2014-01-01

    The benefits of integrating attention and object recognition are investigated. While attention is frequently modeled as a pre-processor for recognition, we investigate the hypothesis that attention is an intrinsic component of recognition and vice-versa. This hypothesis is tested with a recognition model, the hierarchical discriminant saliency network (HDSN), whose layers are top-down saliency detectors, tuned for a visual class according to the principles of discriminant saliency. As a model of neural computation, the HDSN has two possible implementations. In a biologically plausible implementation, all layers comply with the standard neurophysiological model of visual cortex, with sub-layers of simple and complex units that implement a combination of filtering, divisive normalization, pooling, and non-linearities. In a convolutional neural network implementation, all layers are convolutional and implement a combination of filtering, rectification, and pooling. The rectification is performed with a parametric extension of the now popular rectified linear units (ReLUs), whose parameters can be tuned for the detection of target object classes. This enables a number of functional enhancements over neural network models that lack a connection to saliency, including optimal feature denoising mechanisms for recognition, modulation of saliency responses by the discriminant power of the underlying features, and the ability to detect both feature presence and absence. In either implementation, each layer has a precise statistical interpretation, and all parameters are tuned by statistical learning. Each saliency detection layer learns more discriminant saliency templates than its predecessors and higher layers have larger pooling fields. This enables the HDSN to simultaneously achieve high selectivity to target object classes and invariance. The performance of the network in saliency and object recognition tasks is compared to those of models from the biological and

  8. Object recognition with hierarchical discriminant saliency networks.

    PubMed

    Han, Sunhyoung; Vasconcelos, Nuno

    2014-01-01

    The benefits of integrating attention and object recognition are investigated. While attention is frequently modeled as a pre-processor for recognition, we investigate the hypothesis that attention is an intrinsic component of recognition and vice-versa. This hypothesis is tested with a recognition model, the hierarchical discriminant saliency network (HDSN), whose layers are top-down saliency detectors, tuned for a visual class according to the principles of discriminant saliency. As a model of neural computation, the HDSN has two possible implementations. In a biologically plausible implementation, all layers comply with the standard neurophysiological model of visual cortex, with sub-layers of simple and complex units that implement a combination of filtering, divisive normalization, pooling, and non-linearities. In a convolutional neural network implementation, all layers are convolutional and implement a combination of filtering, rectification, and pooling. The rectification is performed with a parametric extension of the now popular rectified linear units (ReLUs), whose parameters can be tuned for the detection of target object classes. This enables a number of functional enhancements over neural network models that lack a connection to saliency, including optimal feature denoising mechanisms for recognition, modulation of saliency responses by the discriminant power of the underlying features, and the ability to detect both feature presence and absence. In either implementation, each layer has a precise statistical interpretation, and all parameters are tuned by statistical learning. Each saliency detection layer learns more discriminant saliency templates than its predecessors and higher layers have larger pooling fields. This enables the HDSN to simultaneously achieve high selectivity to target object classes and invariance. The performance of the network in saliency and object recognition tasks is compared to those of models from the biological and

  9. Object recognition with hierarchical discriminant saliency networks.

    PubMed

    Han, Sunhyoung; Vasconcelos, Nuno

    2014-01-01

    The benefits of integrating attention and object recognition are investigated. While attention is frequently modeled as a pre-processor for recognition, we investigate the hypothesis that attention is an intrinsic component of recognition and vice-versa. This hypothesis is tested with a recognition model, the hierarchical discriminant saliency network (HDSN), whose layers are top-down saliency detectors, tuned for a visual class according to the principles of discriminant saliency. As a model of neural computation, the HDSN has two possible implementations. In a biologically plausible implementation, all layers comply with the standard neurophysiological model of visual cortex, with sub-layers of simple and complex units that implement a combination of filtering, divisive normalization, pooling, and non-linearities. In a convolutional neural network implementation, all layers are convolutional and implement a combination of filtering, rectification, and pooling. The rectification is performed with a parametric extension of the now popular rectified linear units (ReLUs), whose parameters can be tuned for the detection of target object classes. This enables a number of functional enhancements over neural network models that lack a connection to saliency, including optimal feature denoising mechanisms for recognition, modulation of saliency responses by the discriminant power of the underlying features, and the ability to detect both feature presence and absence. In either implementation, each layer has a precise statistical interpretation, and all parameters are tuned by statistical learning. Each saliency detection layer learns more discriminant saliency templates than its predecessors and higher layers have larger pooling fields. This enables the HDSN to simultaneously achieve high selectivity to target object classes and invariance. The performance of the network in saliency and object recognition tasks is compared to those of models from the biological and

  10. Global invariant methods for object recognition

    NASA Astrophysics Data System (ADS)

    Stiller, Peter F.

    2001-11-01

    The general problem of single-view recognition is central to man image understanding and computer vision tasks; so central, that it has been characterized as the holy grail of computer vision. In previous work, we have shown how to approach the general problem of recognizing three dimensional geometric configurations (such as arrangements of lines, points, and conics) from a single two dimensional view, in a manner that is view independent. Our methods make use of advanced mathematical techniques from algebraic geometry, notably the theory of correspondences, and a novel equivariant geometric invariant theory. The machinery gives us a way to understand the relationship that exists between the 3D geometry and its residual in a 2D image. This relationship is shown to be a correspondence in the technical sense of algebraic geometry. Exploiting this, one can compute a set of fundamental equations in 3D and 2D invariants which generate the ideal of the correspondence, and which completely describe the mutual 3D/2D constraints. We have chosen to call these equations object/image equations. They can be exploited in a number of ways. For example, from a given 2D configuration, we can determine a set of non-linear constraints on the geometric invariants of a 3D configurations capable of imaging to the given 2D configuration (features on an object), we can derive a set of equations that constrain the images of that object; helping us to determine if that particular object appears in various images. One previous difficulty has been that the usual numerical geometric invariants get expressed as rational functions of the geometric parameters. As such they are not always defined. This leads to degeneracies in algorithms based on these invariants. We show how to replace these invariants by certain toric subvarieties of Grassmannians where the object/image equations become resultant like expressions for the existence of a non- trivial intersection of these subvarieties with

  11. Training facilitates object recognition in cubist paintings.

    PubMed

    Wiesmann, Martin; Ishai, Alumit

    2010-01-01

    To the naïve observer, cubist paintings contain geometrical forms in which familiar objects are hardly recognizable, even in the presence of a meaningful title. We used fMRI to test whether a short training session about Cubism would facilitate object recognition in paintings by Picasso, Braque and Gris. Subjects, who had no formal art education, were presented with titled or untitled cubist paintings and scrambled images, and performed object recognition tasks. Relative to the control group, trained subjects recognized more objects in the paintings, their response latencies were significantly shorter, and they showed enhanced activation in the parahippocampal cortex, with a parametric increase in the amplitude of the fMRI signal as a function of the number of recognized objects. Moreover, trained subjects were slower to report not recognizing any familiar objects in the paintings and these longer response latencies were correlated with activation in a fronto-parietal network. These findings suggest that trained subjects adopted a visual search strategy and used contextual associations to perform the tasks. Our study supports the proactive brain framework, according to which the brain uses associations to generate predictions. PMID:20224810

  12. A new method of edge detection for object recognition

    USGS Publications Warehouse

    Maddox, Brian G.; Rhew, Benjamin

    2004-01-01

    Traditional edge detection systems function by returning every edge in an input image. This can result in a large amount of clutter and make certain vectorization algorithms less accurate. Accuracy problems can then have a large impact on automated object recognition systems that depend on edge information. A new method of directed edge detection can be used to limit the number of edges returned based on a particular feature. This results in a cleaner image that is easier for vectorization. Vectorized edges from this process could then feed an object recognition system where the edge data would also contain information as to what type of feature it bordered.

  13. Model Based Object Recognition Using LORD LTS-300 Touch Sensor

    NASA Astrophysics Data System (ADS)

    Roach, J. W.; Paripati, P. K.; Wade, M.

    1988-03-01

    This paper reports the result of a model driven touch sensor recognition experiment. The touch sensor employed is a large field tactile array. Object features appropriate for touch sensor recognition are extracted from a geometric model of an object, the dual spherical image. Both geometric and dynamic features are used to identify objects and their position and orientation on the touch sensor. Experiments show that geometric features extracted from the model are effective but that dynamic features must be determined empirically. Correct object identification rates even for very similar objects exceed ninety percent, a success rate much higher than we would have expected from only two-dimensional contact patterns. Position and orientation of objects once identified are very reliable. We conclude that large field tactile sensors could prove very useful in the automatic palletizing problem when object models (from a CAD system, for example) can be utilized.

  14. A smoothness constraint on the development of object recognition.

    PubMed

    Wood, Justin N

    2016-08-01

    Understanding how the brain learns to recognize objects is one of the ultimate goals in the cognitive sciences. To date, however, we have not yet characterized the environmental factors that cause object recognition to emerge in the newborn brain. Here, I present the results of a high-throughput controlled-rearing experiment that examined whether the development of object recognition requires experience with temporally smooth visual objects. When newborn chicks (Gallus gallus) were raised with virtual objects that moved smoothly over time, the chicks developed accurate color recognition, shape recognition, and color-shape binding abilities. In contrast, when newborn chicks were raised with virtual objects that moved non-smoothly over time, the chicks' object recognition abilities were severely impaired. These results provide evidence for a "smoothness constraint" on newborn object recognition. Experience with temporally smooth objects facilitates the development of object recognition. PMID:27208825

  15. Category-specificity in visual object recognition.

    PubMed

    Gerlach, Christian

    2009-06-01

    Are all categories of objects recognized in the same manner visually? Evidence from neuropsychology suggests they are not: some brain damaged patients are more impaired in recognizing natural objects than artefacts whereas others show the opposite impairment. Category-effects have also been demonstrated in neurologically intact subjects, but the findings are contradictory and there is no agreement as to why category-effects arise. This article presents a pre-semantic account of category-effects (PACE) in visual object recognition. PACE assumes two processing stages: shape configuration (the binding of shape elements into elaborate shape descriptions) and selection (among competing representations in visual long-term memory), which are held to be differentially affected by the structural similarity between objects. Drawing on evidence from clinical studies, experimental studies with neurologically intact subjects and functional imaging studies, it is argued that PACE can account for category-effects at both behavioural and neural levels in patients and neurologically intact subjects. The theory also accounts for the way in which category-effects are affected by different task parameters (the degree of perceptual differentiation called for), stimulus characteristics (whether stimuli are presented as silhouettes, full line-drawings, or fragmented forms), stimulus presentation (stimulus exposure duration and position) as well as interactions between these parameters.

  16. Examining Object Location and Object Recognition Memory in Mice

    PubMed Central

    Vogel-Ciernia, Annie; Wood, Marcelo A.

    2014-01-01

    Unit Introduction The ability to store and recall our life experiences defines a person's identity. Consequently, the loss of long-term memory is a particularly devastating part of a variety of cognitive disorders, diseases and injuries. There is a great need to develop therapeutics to treat memory disorders, and thus a variety of animal models and memory paradigms have been developed. Mouse models have been widely used both to study basic disease mechanisms and to evaluate potential drug targets for therapeutic development. The relative ease of genetic manipulation of Mus musculus has led to a wide variety of genetically altered mice that model cognitive disorders ranging from Alzheimer's disease to autism. Rodents, including mice, are particularly adept at encoding and remembering spatial relationships, and these long-term spatial memories are dependent on the medial temporal lobe of the brain. These brain regions are also some of the first and most heavily impacted in disorders of human memory including Alzheimer's disease. Consequently, some of the simplest and most commonly used tests of long-term memory in mice are those that examine memory for objects and spatial relationships. However, many of these tasks, such as Morris water maze and contextual fear conditioning, are dependent upon the encoding and retrieval of emotionally aversive and inherently stressful training events. While these types of memories are important, they do not reflect the typical day-to-day experiences or memories most commonly affected in human disease. In addition, stress hormone release alone can modulate memory and thus obscure or artificially enhance these types of tasks. To avoid these sorts of confounds, we and many others have utilized tasks testing animals’ memory for object location and novel object recognition. These tasks involve exploiting rodents’ innate preference for novelty, and are inherently not stressful. In this protocol we detail how memory for object location

  17. Reader error, object recognition, and visual search

    NASA Astrophysics Data System (ADS)

    Kundel, Harold L.

    2004-05-01

    Small abnormalities such as hairline fractures, lung nodules and breast tumors are missed by competent radiologists with sufficient frequency to make them a matter of concern to the medical community; not only because they lead to litigation but also because they delay patient care. It is very easy to attribute misses to incompetence or inattention. To do so may be placing an unjustified stigma on the radiologists involved and may allow other radiologists to continue a false optimism that it can never happen to them. This review presents some of the fundamentals of visual system function that are relevant to understanding the search for and the recognition of small targets embedded in complicated but meaningful backgrounds like chests and mammograms. It presents a model for visual search that postulates a pre-attentive global analysis of the retinal image followed by foveal checking fixations and eventually discovery scanning. The model will be used to differentiate errors of search, recognition and decision making. The implications for computer aided diagnosis and for functional workstation design are discussed.

  18. A novel multi-view object recognition in complex background

    NASA Astrophysics Data System (ADS)

    Chang, Yongxin; Yu, Huapeng; Xu, Zhiyong; Fu, Chengyu; Gao, Chunming

    2015-02-01

    Recognizing objects from arbitrary aspects is always a highly challenging problem in computer vision, and most existing algorithms mainly focus on a specific viewpoint research. Hence, in this paper we present a novel recognizing framework based on hierarchical representation, part-based method and learning in order to recognize objects from different viewpoints. The learning evaluates the model's mistakes and feeds it back the detector to avid the same mistakes in the future. The principal idea is to extract intrinsic viewpoint invariant features from the unseen poses of object, and then to take advantage of these shared appearance features to support recognition combining with the improved multiple view model. Compared with other recognition models, the proposed approach can efficiently tackle multi-view problem and promote the recognition versatility of our system. For an quantitative valuation The novel algorithm has been tested on several benchmark datasets such as Caltech 101 and PASCAL VOC 2010. The experimental results validate that our approach can recognize objects more precisely and the performance outperforms others single view recognition methods.

  19. Object Recognition using Feature- and Color-Based Methods

    NASA Technical Reports Server (NTRS)

    Duong, Tuan; Duong, Vu; Stubberud, Allen

    2008-01-01

    An improved adaptive method of processing image data in an artificial neural network has been developed to enable automated, real-time recognition of possibly moving objects under changing (including suddenly changing) conditions of illumination and perspective. The method involves a combination of two prior object-recognition methods one based on adaptive detection of shape features and one based on adaptive color segmentation to enable recognition in situations in which either prior method by itself may be inadequate. The chosen prior feature-based method is known as adaptive principal-component analysis (APCA); the chosen prior color-based method is known as adaptive color segmentation (ACOSE). These methods are made to interact with each other in a closed-loop system to obtain an optimal solution of the object-recognition problem in a dynamic environment. One of the results of the interaction is to increase, beyond what would otherwise be possible, the accuracy of the determination of a region of interest (containing an object that one seeks to recognize) within an image. Another result is to provide a minimized adaptive step that can be used to update the results obtained by the two component methods when changes of color and apparent shape occur. The net effect is to enable the neural network to update its recognition output and improve its recognition capability via an adaptive learning sequence. In principle, the improved method could readily be implemented in integrated circuitry to make a compact, low-power, real-time object-recognition system. It has been proposed to demonstrate the feasibility of such a system by integrating a 256-by-256 active-pixel sensor with APCA, ACOSE, and neural processing circuitry on a single chip. It has been estimated that such a system on a chip would have a volume no larger than a few cubic centimeters, could operate at a rate as high as 1,000 frames per second, and would consume in the order of milliwatts of power.

  20. Automatic Recognition of Object Names in Literature

    NASA Astrophysics Data System (ADS)

    Bonnin, C.; Lesteven, S.; Derriere, S.; Oberto, A.

    2008-08-01

    SIMBAD is a database of astronomical objects that provides (among other things) their bibliographic references in a large number of journals. Currently, these references have to be entered manually by librarians who read each paper. To cope with the increasing number of papers, CDS develops a tool to assist the librarians in their work, taking advantage of the Dictionary of Nomenclature of Celestial Objects, which keeps track of object acronyms and of their origin. The program searches for object names directly in PDF documents by comparing the words with all the formats stored in the Dictionary of Nomenclature. It also searches for variable star names based on constellation names and for a large list of usual names such as Aldebaran or the Crab. Object names found in the documents often correspond to several astronomical objects. The system retrieves all possible matches, displays them with their object type given by SIMBAD, and lets the librarian make the final choice. The bibliographic reference can then be automatically added to the object identifiers in the database. Besides, the systematic usage of the Dictionary of Nomenclature, which is updated manually, permitted to automatically check it and to detect errors and inconsistencies. Last but not least, the program collects some additional information such as the position of the object names in the document (in the title, subtitle, abstract, table, figure caption...) and their number of occurrences. In the future, this will permit to calculate the 'weight' of an object in a reference and to provide SIMBAD users with an important new information, which will help them to find the most relevant papers in the object reference list.

  1. Hydrodynamic Object Recognition: When Multipoles Count

    NASA Astrophysics Data System (ADS)

    Sichert, Andreas B.; Bamler, Robert; van Hemmen, J. Leo

    2009-02-01

    The lateral-line system is a unique mechanosensory facility of aquatic animals that enables them not only to localize prey, predator, obstacles, and conspecifics, but also to recognize hydrodynamic objects. Here we present an explicit model explaining how aquatic animals such as fish can distinguish differently shaped submerged moving objects. Our model is based on the hydrodynamic multipole expansion and uses the unambiguous set of multipole components to identify the corresponding object. Furthermore, we show that within the natural range of one fish length the velocity field contains far more information than that due to a dipole. Finally, the model we present is easy to implement both neuronally and technically, and agrees well with available neuronal, physiological, and behavioral data on the lateral-line system.

  2. Feature based recognition of submerged objects in holographic imagery

    NASA Astrophysics Data System (ADS)

    Ratto, Christopher R.; Beagley, Nathaniel; Baldwin, Kevin C.; Shipley, Kara R.; Sternberger, Wayne I.

    2014-05-01

    The ability to autonomously sense and characterize underwater objects in situ is desirable in applications of unmanned underwater vehicles (UUVs). In this work, underwater object recognition was explored using a digital holographic system. Two experiments were performed in which several objects of varying size, shape, and material were submerged in a 43,000 gallon test tank. Holograms were collected from each object at multiple distances and orientations, with the imager located either outside the tank (looking through a porthole) or submerged (looking downward). The resultant imagery from these holograms was preprocessed to improve dynamic range, mitigate speckle, and segment out the image of the object. A collection of feature descriptors were then extracted from the imagery to characterize various object properties (e.g., shape, reflectivity, texture). The features extracted from images of multiple objects, collected at different imaging geometries, were then used to train statistical models for object recognition tasks. The resulting classification models were used to perform object classification as well as estimation of various parameters of the imaging geometry. This information can then be used to inform the design of autonomous sensing algorithms for UUVs employing holographic imagers.

  3. Object recognition by triaural perception on a mobile robot

    NASA Astrophysics Data System (ADS)

    Peremans, Herbert; Van Campenhout, Jan M.

    1993-05-01

    To overcome some of the problems associated with the use of ultrasonic sensors for navigation purposes, we propose a measurement system composed of three ultrasonic sensors, one transmitting and three receiving, placed on a moving vehicle. By triangulation this tri-aural sensor is able to determine the position, both distance and bearing, of the objects in the field of view. In this paper, we derive a statistical test which combines consecutive sightings by the moving sensor, of the same object to determine whether it is an edge, a plane or a corner. This test is formulated as a sequential test which guarantees that the object will be recognized after the minimal number of measurements given predetermined error probabilities. We include experimental data showing the object recognition capabilities of the system.

  4. A hybrid learning approach for better recognition of visual objects

    SciTech Connect

    Imam, I.F.; Gutta, S.

    1996-12-31

    Real world images often contain similar objects but with different rotations, noise, or other visual alterations. Vision systems should be able to recognize objects regardless of these visual alterations. This paper presents a novel approach for learning optimized structures of classifiers for recognizing visual objects regardless of certain types of visual alterations. The approach consists of two phases. The first phase is concerned with learning classifications of a set of standard and altered objects. The second phase is concerned with discovering an optimized structure of classifiers for recognizing objects from unseen images. This paper presents an application of this approach to a domain of 15 classes of hand gestures. The experimental results show significant improvement in the recognition rate rather than using a single classifier or multiple classifiers with thresholds.

  5. Temporal scales of auditory objects underlying birdsong vocal recognition

    PubMed Central

    Gentner, Timothy Q.

    2008-01-01

    Vocal recognition is common among songbirds, and provides an excellent model system to study the perceptual and neurobiological mechanisms for processing natural vocal communication signals. Male European starlings, a species of songbird, learn to recognize the songs of multiple conspecific males by attending to stereotyped acoustic patterns, and these learned patterns elicit selective neuronal responses in auditory forebrain neurons. The present study investigates the perceptual grouping of spectrotemporal acoustic patterns in starling song at multiple temporal scales. The results show that permutations in sequencing of submotif acoustic features have significant effects on song recognition, and that these effects are specific to songs that comprise learned motifs. The observations suggest that (1) motifs form auditory objects embedded in a hierarchy of acoustic patterns, (2) that object-based song perception emerges without explicit reinforcement, and (3) that multiple temporal scales within the acoustic pattern hierarchy convey information about the individual identity of the singer. The authors discuss the results in the context of auditory object formation and talker recognition. PMID:18681620

  6. Systemic L-Kynurenine sulfate administration disrupts object recognition memory, alters open field behavior and decreases c-Fos immunopositivity in C57Bl/6 mice

    PubMed Central

    Varga, Dániel; Herédi, Judit; Kánvási, Zita; Ruszka, Marian; Kis, Zsolt; Ono, Etsuro; Iwamori, Naoki; Iwamori, Tokuko; Takakuwa, Hiroki; Vécsei, László; Toldi, József; Gellért, Levente

    2015-01-01

    L-Kynurenine (L-KYN) is a central metabolite of tryptophan degradation through the kynurenine pathway (KP). The systemic administration of L-KYN sulfate (L-KYNs) leads to a rapid elevation of the neuroactive KP metabolite kynurenic acid (KYNA). An elevated level of KYNA may have multiple effects on the synaptic transmission, resulting in complex behavioral changes, such as hypoactivity or spatial working memory deficits. These results emerged from studies that focused on rats, after low-dose L-KYNs treatment. However, in several studies neuroprotection was achieved through the administration of high-dose L-KYNs. In the present study, our aim was to investigate whether the systemic administration of a high dose of L-KYNs (300 mg/bwkg; i.p.) would produce alterations in behavioral tasks (open field or object recognition) in C57Bl/6j mice. To evaluate the changes in neuronal activity after L-KYNs treatment, in a separate group of animals we estimated c-Fos expression levels in the corresponding subcortical brain areas. The L-KYNs treatment did not affect the general ambulatory activity of C57Bl/6j mice, whereas it altered their moving patterns, elevating the movement velocity and resting time. Additionally, it seemed to increase anxiety-like behavior, as peripheral zone preference of the open field arena emerged and the rearing activity was attenuated. The treatment also completely abolished the formation of object recognition memory and resulted in decreases in the number of c-Fos-immunopositive-cells in the dorsal part of the striatum and in the CA1 pyramidal cell layer of the hippocampus. We conclude that a single exposure to L-KYNs leads to behavioral disturbances, which might be related to the altered basal c-Fos protein expression in C57Bl/6j mice. PMID:26136670

  7. Fast neuromimetic object recognition using FPGA outperforms GPU implementations.

    PubMed

    Orchard, Garrick; Martin, Jacob G; Vogelstein, R Jacob; Etienne-Cummings, Ralph

    2013-08-01

    Recognition of objects in still images has traditionally been regarded as a difficult computational problem. Although modern automated methods for visual object recognition have achieved steadily increasing recognition accuracy, even the most advanced computational vision approaches are unable to obtain performance equal to that of humans. This has led to the creation of many biologically inspired models of visual object recognition, among them the hierarchical model and X (HMAX) model. HMAX is traditionally known to achieve high accuracy in visual object recognition tasks at the expense of significant computational complexity. Increasing complexity, in turn, increases computation time, reducing the number of images that can be processed per unit time. In this paper we describe how the computationally intensive and biologically inspired HMAX model for visual object recognition can be modified for implementation on a commercial field-programmable aate Array, specifically the Xilinx Virtex 6 ML605 evaluation board with XC6VLX240T FPGA. We show that with minor modifications to the traditional HMAX model we can perform recognition on images of size 128 × 128 pixels at a rate of 190 images per second with a less than 1% loss in recognition accuracy in both binary and multiclass visual object recognition tasks.

  8. Visual Object Recognition and Tracking of Tools

    NASA Technical Reports Server (NTRS)

    English, James; Chang, Chu-Yin; Tardella, Neil

    2011-01-01

    A method has been created to automatically build an algorithm off-line, using computer-aided design (CAD) models, and to apply this at runtime. The object type is discriminated, and the position and orientation are identified. This system can work with a single image and can provide improved performance using multiple images provided from videos. The spatial processing unit uses three stages: (1) segmentation; (2) initial type, pose, and geometry (ITPG) estimation; and (3) refined type, pose, and geometry (RTPG) calculation. The image segmentation module files all the tools in an image and isolates them from the background. For this, the system uses edge-detection and thresholding to find the pixels that are part of a tool. After the pixels are identified, nearby pixels are grouped into blobs. These blobs represent the potential tools in the image and are the product of the segmentation algorithm. The second module uses matched filtering (or template matching). This approach is used for condensing synthetic images using an image subspace that captures key information. Three degrees of orientation, three degrees of position, and any number of degrees of freedom in geometry change are included. To do this, a template-matching framework is applied. This framework uses an off-line system for calculating template images, measurement images, and the measurements of the template images. These results are used online to match segmented tools against the templates. The final module is the RTPG processor. Its role is to find the exact states of the tools given initial conditions provided by the ITPG module. The requirement that the initial conditions exist allows this module to make use of a local search (whereas the ITPG module had global scope). To perform the local search, 3D model matching is used, where a synthetic image of the object is created and compared to the sensed data. The availability of low-cost PC graphics hardware allows rapid creation of synthetic images

  9. An Efficient Bayesian Approach to Exploit the Context of Object-Action Interaction for Object Recognition

    PubMed Central

    Yoon, Sungbaek; Park, Hyunjin; Yi, Juneho

    2016-01-01

    This research features object recognition that exploits the context of object-action interaction to enhance the recognition performance. Since objects have specific usages, and human actions corresponding to these usages can be associated with these objects, human actions can provide effective information for object recognition. When objects from different categories have similar appearances, the human action associated with each object can be very effective in resolving ambiguities related to recognizing these objects. We propose an efficient method that integrates human interaction with objects into a form of object recognition. We represent human actions by concatenating poselet vectors computed from key frames and learn the probabilities of objects and actions using random forest and multi-class AdaBoost algorithms. Our experimental results show that poselet representation of human actions is quite effective in integrating human action information into object recognition. PMID:27347977

  10. Manipulability and object recognition: is manipulability a semantic feature?

    PubMed

    Campanella, Fabio; Shallice, Tim

    2011-02-01

    Several lines of evidence exist, coming from neuropsychology, neuroimaging and behavioural investigations on healthy subjects, suggesting that an interaction might exist between the systems devoted to object identification and those devoted to online object-directed actions and that the way an object is acted upon (manipulability) might indeed influence object recognition. In this series of experiments on speeded word-to-picture-matching tasks, it is shown how the presentation of pairs of objects sharing similar manipulation causes greater interference with respect to objects sharing only visual similarity (experiment 1). Moreover, (experiment 2) it is shown how the repeated presentation of pairs of objects sharing a similar type of manipulation leads to a 'negative' serial position effect, with the number of errors increasing across presentations, a behaviour that is typically found in patients with access deficits to semantic representations. By contrast, the repeated presentation of pairs of objects sharing only visual similarity leads to an opposite 'positive' serial position effect, with errors decreasing across presentations. It is argued that a negative serial position effect is linked to interference occurring within the semantic system, and therefore that the way an object is manipulated is indeed a semantic feature, critical in defining manipulable object properties at a semantic level. To our knowledge, this constitutes the first direct evidence of manipulability being a semantic dimension. The results are discussed in the light of current models of semantic memory organization.

  11. Object recognition and localization: the role of tactile sensors.

    PubMed

    Aggarwal, Achint; Kirchner, Frank

    2014-01-01

    Tactile sensors, because of their intrinsic insensitivity to lighting conditions and water turbidity, provide promising opportunities for augmenting the capabilities of vision sensors in applications involving object recognition and localization. This paper presents two approaches for haptic object recognition and localization for ground and underwater environments. The first approach called Batch Ransac and Iterative Closest Point augmented Particle Filter (BRICPPF) is based on an innovative combination of particle filters, Iterative-Closest-Point algorithm, and a feature-based Random Sampling and Consensus (RANSAC) algorithm for database matching. It can handle a large database of 3D-objects of complex shapes and performs a complete six-degree-of-freedom localization of static objects. The algorithms are validated by experimentation in ground and underwater environments using real hardware. To our knowledge this is the first instance of haptic object recognition and localization in underwater environments. The second approach is biologically inspired, and provides a close integration between exploration and recognition. An edge following exploration strategy is developed that receives feedback from the current state of recognition. A recognition by parts approach is developed which uses the BRICPPF for object sub-part recognition. Object exploration is either directed to explore a part until it is successfully recognized, or is directed towards new parts to endorse the current recognition belief. This approach is validated by simulation experiments. PMID:24553087

  12. Object Recognition and Localization: The Role of Tactile Sensors

    PubMed Central

    Aggarwal, Achint; Kirchner, Frank

    2014-01-01

    Tactile sensors, because of their intrinsic insensitivity to lighting conditions and water turbidity, provide promising opportunities for augmenting the capabilities of vision sensors in applications involving object recognition and localization. This paper presents two approaches for haptic object recognition and localization for ground and underwater environments. The first approach called Batch Ransac and Iterative Closest Point augmented Particle Filter (BRICPPF) is based on an innovative combination of particle filters, Iterative-Closest-Point algorithm, and a feature-based Random Sampling and Consensus (RANSAC) algorithm for database matching. It can handle a large database of 3D-objects of complex shapes and performs a complete six-degree-of-freedom localization of static objects. The algorithms are validated by experimentation in ground and underwater environments using real hardware. To our knowledge this is the first instance of haptic object recognition and localization in underwater environments. The second approach is biologically inspired, and provides a close integration between exploration and recognition. An edge following exploration strategy is developed that receives feedback from the current state of recognition. A recognition by parts approach is developed which uses the BRICPPF for object sub-part recognition. Object exploration is either directed to explore a part until it is successfully recognized, or is directed towards new parts to endorse the current recognition belief. This approach is validated by simulation experiments. PMID:24553087

  13. Object recognition and localization: the role of tactile sensors.

    PubMed

    Aggarwal, Achint; Kirchner, Frank

    2014-01-01

    Tactile sensors, because of their intrinsic insensitivity to lighting conditions and water turbidity, provide promising opportunities for augmenting the capabilities of vision sensors in applications involving object recognition and localization. This paper presents two approaches for haptic object recognition and localization for ground and underwater environments. The first approach called Batch Ransac and Iterative Closest Point augmented Particle Filter (BRICPPF) is based on an innovative combination of particle filters, Iterative-Closest-Point algorithm, and a feature-based Random Sampling and Consensus (RANSAC) algorithm for database matching. It can handle a large database of 3D-objects of complex shapes and performs a complete six-degree-of-freedom localization of static objects. The algorithms are validated by experimentation in ground and underwater environments using real hardware. To our knowledge this is the first instance of haptic object recognition and localization in underwater environments. The second approach is biologically inspired, and provides a close integration between exploration and recognition. An edge following exploration strategy is developed that receives feedback from the current state of recognition. A recognition by parts approach is developed which uses the BRICPPF for object sub-part recognition. Object exploration is either directed to explore a part until it is successfully recognized, or is directed towards new parts to endorse the current recognition belief. This approach is validated by simulation experiments.

  14. An ERP Study on Self-Relevant Object Recognition

    ERIC Educational Resources Information Center

    Miyakoshi, Makoto; Nomura, Michio; Ohira, Hideki

    2007-01-01

    We performed an event-related potential study to investigate the self-relevance effect in object recognition. Three stimulus categories were prepared: SELF (participant's own objects), FAMILIAR (disposable and public objects, defined as objects with less-self-relevant familiarity), and UNFAMILIAR (others' objects). The participants' task was to…

  15. Infants' Recognition of Objects Using Canonical Color

    ERIC Educational Resources Information Center

    Kimura, Atsushi; Wada, Yuji; Yang, Jiale; Otsuka, Yumiko; Dan, Ippeita; Masuda, Tomohiro; Kanazawa, So; Yamaguchi, Masami K.

    2010-01-01

    We explored infants' ability to recognize the canonical colors of daily objects, including two color-specific objects (human face and fruit) and a non-color-specific object (flower), by using a preferential looking technique. A total of 58 infants between 5 and 8 months of age were tested with a stimulus composed of two color pictures of an object…

  16. Mechanisms of object recognition: what we have learned from pigeons

    PubMed Central

    Soto, Fabian A.; Wasserman, Edward A.

    2014-01-01

    Behavioral studies of object recognition in pigeons have been conducted for 50 years, yielding a large body of data. Recent work has been directed toward synthesizing this evidence and understanding the visual, associative, and cognitive mechanisms that are involved. The outcome is that pigeons are likely to be the non-primate species for which the computational mechanisms of object recognition are best understood. Here, we review this research and suggest that a core set of mechanisms for object recognition might be present in all vertebrates, including pigeons and people, making pigeons an excellent candidate model to study the neural mechanisms of object recognition. Behavioral and computational evidence suggests that error-driven learning participates in object category learning by pigeons and people, and recent neuroscientific research suggests that the basal ganglia, which are homologous in these species, may implement error-driven learning of stimulus-response associations. Furthermore, learning of abstract category representations can be observed in pigeons and other vertebrates. Finally, there is evidence that feedforward visual processing, a central mechanism in models of object recognition in the primate ventral stream, plays a role in object recognition by pigeons. We also highlight differences between pigeons and people in object recognition abilities, and propose candidate adaptive specializations which may explain them, such as holistic face processing and rule-based category learning in primates. From a modern comparative perspective, such specializations are to be expected regardless of the model species under study. The fact that we have a good idea of which aspects of object recognition differ in people and pigeons should be seen as an advantage over other animal models. From this perspective, we suggest that there is much to learn about human object recognition from studying the “simple” brains of pigeons. PMID:25352784

  17. Declining object recognition performance in semantic dementia: A case for stored visual object representations.

    PubMed

    Tree, Jeremy J; Playfoot, David

    2015-01-01

    The role of the semantic system in recognizing objects is a matter of debate. Connectionist theories argue that it is impossible for a participant to determine that an object is familiar to them without recourse to a semantic hub; localist theories state that accessing a stored representation of the visual features of the object is sufficient for recognition. We examine this issue through the longitudinal study of two cases of semantic dementia, a neurodegenerative disorder characterized by a progressive degradation of the semantic system. The cases in this paper do not conform to the "common" pattern of object recognition performance in semantic dementia described by Rogers, T. T., Lambon Ralph, M. A., Hodges, J. R., & Patterson, K. (2004). Natural selection: The impact of semantic impairment on lexical and object decision. Cognitive Neuropsychology, 21, 331-352., and show no systematic relationship between severity of semantic impairment and success in object decision. We argue that these data are inconsistent with the connectionist position but can be easily reconciled with localist theories that propose stored structural descriptions of objects outside of the semantic system. PMID:27355607

  18. Eye movements during object recognition in visual agnosia.

    PubMed

    Charles Leek, E; Patterson, Candy; Paul, Matthew A; Rafal, Robert; Cristino, Filipe

    2012-07-01

    This paper reports the first ever detailed study about eye movement patterns during single object recognition in visual agnosia. Eye movements were recorded in a patient with an integrative agnosic deficit during two recognition tasks: common object naming and novel object recognition memory. The patient showed normal directional biases in saccades and fixation dwell times in both tasks and was as likely as controls to fixate within object bounding contour regardless of recognition accuracy. In contrast, following initial saccades of similar amplitude to controls, the patient showed a bias for short saccades. In object naming, but not in recognition memory, the similarity of the spatial distributions of patient and control fixations was modulated by recognition accuracy. The study provides new evidence about how eye movements can be used to elucidate the functional impairments underlying object recognition deficits. We argue that the results reflect a breakdown in normal functional processes involved in the integration of shape information across object structure during the visual perception of shape.

  19. Multiple-View Object Recognition in Smart Camera Networks

    NASA Astrophysics Data System (ADS)

    Yang, Allen Y.; Maji, Subhransu; Christoudias, C. Mario; Darrell, Trevor; Malik, Jitendra; Sastry, S. Shankar

    We study object recognition in low-power, low-bandwidth smart camera networks. The ability to perform robust object recognition is crucial for applications such as visual surveillance to track and identify objects of interest, and overcome visual nuisances such as occlusion and pose variations between multiple camera views. To accommodate limited bandwidth between the cameras and the base-station computer, the method utilizes the available computational power on the smart sensors to locally extract SIFT-type image features to represent individual camera views. We show that between a network of cameras, high-dimensional SIFT histograms exhibit a joint sparse pattern corresponding to a set of shared features in 3-D. Such joint sparse patterns can be explicitly exploited to encode the distributed signal via random projections. At the network station, multiple decoding schemes are studied to simultaneously recover the multiple-view object features based on a distributed compressive sensing theory. The system has been implemented on the Berkeley CITRIC smart camera platform. The efficacy of the algorithm is validated through extensive simulation and experiment.

  20. Category-Specificity in Visual Object Recognition

    ERIC Educational Resources Information Center

    Gerlach, Christian

    2009-01-01

    Are all categories of objects recognized in the same manner visually? Evidence from neuropsychology suggests they are not: some brain damaged patients are more impaired in recognizing natural objects than artefacts whereas others show the opposite impairment. Category-effects have also been demonstrated in neurologically intact subjects, but the…

  1. A Taxonomy of 3D Occluded Objects Recognition Techniques

    NASA Astrophysics Data System (ADS)

    Soleimanizadeh, Shiva; Mohamad, Dzulkifli; Saba, Tanzila; Al-ghamdi, Jarallah Saleh

    2016-03-01

    The overall performances of object recognition techniques under different condition (e.g., occlusion, viewpoint, and illumination) have been improved significantly in recent years. New applications and hardware are shifted towards digital photography, and digital media. This faces an increase in Internet usage requiring object recognition for certain applications; particularly occulded objects. However occlusion is still an issue unhandled, interlacing the relations between extracted feature points through image, research is going on to develop efficient techniques and easy to use algorithms that would help users to source images; this need to overcome problems and issues regarding occlusion. The aim of this research is to review recognition occluded objects algorithms and figure out their pros and cons to solve the occlusion problem features, which are extracted from occluded object to distinguish objects from other co-existing objects by determining the new techniques, which could differentiate the occluded fragment and sections inside an image.

  2. The subjective experience of object recognition: comparing metacognition for object detection and object categorization.

    PubMed

    Meuwese, Julia D I; van Loon, Anouk M; Lamme, Victor A F; Fahrenfort, Johannes J

    2014-05-01

    Perceptual decisions seem to be made automatically and almost instantly. Constructing a unitary subjective conscious experience takes more time. For example, when trying to avoid a collision with a car on a foggy road you brake or steer away in a reflex, before realizing you were in a near accident. This subjective aspect of object recognition has been given little attention. We used metacognition (assessed with confidence ratings) to measure subjective experience during object detection and object categorization for degraded and masked objects, while objective performance was matched. Metacognition was equal for degraded and masked objects, but categorization led to higher metacognition than did detection. This effect turned out to be driven by a difference in metacognition for correct rejection trials, which seemed to be caused by an asymmetry of the distractor stimulus: It does not contain object-related information in the detection task, whereas it does contain such information in the categorization task. Strikingly, this asymmetry selectively impacted metacognitive ability when objective performance was matched. This finding reveals a fundamental difference in how humans reflect versus act on information: When matching the amount of information required to perform two tasks at some objective level of accuracy (acting), metacognitive ability (reflecting) is still better in tasks that rely on positive evidence (categorization) than in tasks that rely more strongly on an absence of evidence (detection).

  3. The subjective experience of object recognition: comparing metacognition for object detection and object categorization.

    PubMed

    Meuwese, Julia D I; van Loon, Anouk M; Lamme, Victor A F; Fahrenfort, Johannes J

    2014-05-01

    Perceptual decisions seem to be made automatically and almost instantly. Constructing a unitary subjective conscious experience takes more time. For example, when trying to avoid a collision with a car on a foggy road you brake or steer away in a reflex, before realizing you were in a near accident. This subjective aspect of object recognition has been given little attention. We used metacognition (assessed with confidence ratings) to measure subjective experience during object detection and object categorization for degraded and masked objects, while objective performance was matched. Metacognition was equal for degraded and masked objects, but categorization led to higher metacognition than did detection. This effect turned out to be driven by a difference in metacognition for correct rejection trials, which seemed to be caused by an asymmetry of the distractor stimulus: It does not contain object-related information in the detection task, whereas it does contain such information in the categorization task. Strikingly, this asymmetry selectively impacted metacognitive ability when objective performance was matched. This finding reveals a fundamental difference in how humans reflect versus act on information: When matching the amount of information required to perform two tasks at some objective level of accuracy (acting), metacognitive ability (reflecting) is still better in tasks that rely on positive evidence (categorization) than in tasks that rely more strongly on an absence of evidence (detection). PMID:24554231

  4. Object Recognition and Random Image Structure Evolution

    ERIC Educational Resources Information Center

    Sadr, Jvid; Sinha, Pawan

    2004-01-01

    We present a technique called Random Image Structure Evolution (RISE) for use in experimental investigations of high-level visual perception. Potential applications of RISE include the quantitative measurement of perceptual hysteresis and priming, the study of the neural substrates of object perception, and the assessment and detection of subtle…

  5. Crowding: a cortical constraint on object recognition.

    PubMed

    Pelli, Denis G

    2008-08-01

    The external world is mapped retinotopically onto the primary visual cortex (V1). We show here that objects in the world, unless they are very dissimilar, can be recognized only if they are sufficiently separated in visual cortex: specifically, in V1, at least 6mm apart in the radial direction (increasing eccentricity) or 1mm apart in the circumferential direction (equal eccentricity). Objects closer together than this critical spacing are perceived as an unidentifiable jumble. This is called 'crowding'. It severely limits visual processing, including speed of reading and searching. The conclusion about visual cortex rests on three findings. First, psychophysically, the necessary 'critical' spacing, in the visual field, is proportional to (roughly half) the eccentricity of the objects. Second, the critical spacing is independent of the size and kind of object. Third, anatomically, the representation of the visual field on the cortical surface is such that the position in V1 (and several other areas) is the logarithm of eccentricity in the visual field. Furthermore, we show that much of this can be accounted for by supposing that each 'combining field', defined by the critical spacing measurements, is implemented by a fixed number of cortical neurons.

  6. Acquired prosopagnosia with spared within-class object recognition but impaired recognition of degraded basic-level objects.

    PubMed

    Rezlescu, Constantin; Pitcher, David; Duchaine, Brad

    2012-01-01

    We present a new case of acquired prosopagnosia resulting from extensive lesions predominantly in the right occipitotemporal cortex. Functional brain imaging revealed atypical activation of all core face areas in the right hemisphere, with reduced signal difference between faces and objects compared to controls. In contrast, Herschel's lateral occipital complex showed normal activation to objects. Behaviourally, Herschel is severely impaired with the recognition of familiar faces, discrimination between unfamiliar identities, and the perception of facial expression and gender. Notably, his visual recognition deficits are largely restricted to faces, suggesting that the damaged mechanisms are face-specific. He showed normal recognition memory for a wide variety of object classes in several paradigms, normal ability to discriminate between highly similar items within a novel object category, and intact ability to name basic objects (except four-legged animals). Furthermore, Herschel displayed a normal face composite effect and typical global advantage and global interference effects in the Navon task, suggesting spared integration of both face and nonface information. Nevertheless, he failed visual closure tests requiring recognition of basic objects from degraded images. This abnormality in basic object recognition is at odds with his spared within-class recognition and presents a challenge to hierarchical models of object perception.

  7. Object locating system

    DOEpatents

    Novak, James L.; Petterson, Ben

    1998-06-09

    A sensing system locates an object by sensing the object's effect on electric fields. The object's effect on the mutual capacitance of electrode pairs varies according to the distance between the object and the electrodes. A single electrode pair can sense the distance from the object to the electrodes. Multiple electrode pairs can more precisely locate the object in one or more dimensions.

  8. Object locating system

    DOEpatents

    Novak, J.L.; Petterson, B.

    1998-06-09

    A sensing system locates an object by sensing the object`s effect on electric fields. The object`s effect on the mutual capacitance of electrode pairs varies according to the distance between the object and the electrodes. A single electrode pair can sense the distance from the object to the electrodes. Multiple electrode pairs can more precisely locate the object in one or more dimensions. 12 figs.

  9. Developmental Commonalities between Object and Face Recognition in Adolescence

    PubMed Central

    Jüttner, Martin; Wakui, Elley; Petters, Dean; Davidoff, Jules

    2016-01-01

    In the visual perception literature, the recognition of faces has often been contrasted with that of non-face objects, in terms of differences with regard to the role of parts, part relations and holistic processing. However, recent evidence from developmental studies has begun to blur this sharp distinction. We review evidence for a protracted development of object recognition that is reminiscent of the well-documented slow maturation observed for faces. The prolonged development manifests itself in a retarded processing of metric part relations as opposed to that of individual parts and offers surprising parallels to developmental accounts of face recognition, even though the interpretation of the data is less clear with regard to holistic processing. We conclude that such results might indicate functional commonalities between the mechanisms underlying the recognition of faces and non-face objects, which are modulated by different task requirements in the two stimulus domains. PMID:27014176

  10. Object similarity affects the perceptual strategy underlying invariant visual object recognition in rats.

    PubMed

    Rosselli, Federica B; Alemi, Alireza; Ansuini, Alessio; Zoccolan, Davide

    2015-01-01

    In recent years, a number of studies have explored the possible use of rats as models of high-level visual functions. One central question at the root of such an investigation is to understand whether rat object vision relies on the processing of visual shape features or, rather, on lower-order image properties (e.g., overall brightness). In a recent study, we have shown that rats are capable of extracting multiple features of an object that are diagnostic of its identity, at least when those features are, structure-wise, distinct enough to be parsed by the rat visual system. In the present study, we have assessed the impact of object structure on rat perceptual strategy. We trained rats to discriminate between two structurally similar objects, and compared their recognition strategies with those reported in our previous study. We found that, under conditions of lower stimulus discriminability, rat visual discrimination strategy becomes more view-dependent and subject-dependent. Rats were still able to recognize the target objects, in a way that was largely tolerant (i.e., invariant) to object transformation; however, the larger structural and pixel-wise similarity affected the way objects were processed. Compared to the findings of our previous study, the patterns of diagnostic features were: (i) smaller and more scattered; (ii) only partially preserved across object views; and (iii) only partially reproducible across rats. On the other hand, rats were still found to adopt a multi-featural processing strategy and to make use of part of the optimal discriminatory information afforded by the two objects. Our findings suggest that, as in humans, rat invariant recognition can flexibly rely on either view-invariant representations of distinctive object features or view-specific object representations, acquired through learning. PMID:25814936

  11. Testing conditions for viewpoint invariance in object recognition.

    PubMed

    Hayward, W G; Tarr, M J

    1997-10-01

    Based on the geon structural description approach, I. Biederman and P.C. Gerhardstein (1993) proposed 3 conditions under which object recognition is predicted to be viewpoint invariant. Two experiments are reported that satisfied all 3 criteria yet revealed performance that was clearly viewpoint dependent. Experiment 1 demonstrated that for both sequential matching and naming tasks, recognition of qualitatively distinct objects became progressively longer and less accurate as the viewpoint difference between study and test viewpoints increased. Experiment 2 demonstrated that for single-part objects, larger effects of viewpoint occurred when there was a change in the visible structure, indicating sensitivity to qualitative features in the image, not geon structural descriptions. These results suggest that the conditions proposed by I. Biederman and P.C. Gerhardstein are not generally applicable, the recognition of qualitatively distinct objects often relies on viewpoint-dependent mechanisms, and the molar features of view-based mechanisms appear to be image features rather than geons. PMID:9411023

  12. Spontaneous Object Recognition Memory in Aged Rats: Complexity versus Similarity

    ERIC Educational Resources Information Center

    Gamiz, Fernando; Gallo, Milagros

    2012-01-01

    Previous work on the effect of aging on spontaneous object recognition (SOR) memory tasks in rats has yielded controversial results. Although the results at long-retention intervals are consistent, conflicting results have been reported at shorter delays. We have assessed the potential relevance of the type of object used in the performance of…

  13. The Neural Regions Sustaining Episodic Encoding and Recognition of Objects

    ERIC Educational Resources Information Center

    Hofer, Alex; Siedentopf, Christian M.; Ischebeck, Anja; Rettenbacher, Maria A.; Widschwendter, Christian G.; Verius, Michael; Golaszewski, Stefan M.; Koppelstaetter, Florian; Felber, Stephan; Wolfgang Fleischhacker, W.

    2007-01-01

    In this functional MRI experiment, encoding of objects was associated with activation in left ventrolateral prefrontal/insular and right dorsolateral prefrontal and fusiform regions as well as in the left putamen. By contrast, correct recognition of previously learned objects (R judgments) produced activation in left superior frontal, bilateral…

  14. Computational integral-imaging reconstruction-based 3-D volumetric target object recognition by using a 3-D reference object.

    PubMed

    Kim, Seung-Cheol; Park, Seok-Chan; Kim, Eun-Soo

    2009-12-01

    In this paper, we propose a novel computational integral-imaging reconstruction (CIIR)-based three-dimensional (3-D) image correlator system for the recognition of 3-D volumetric objects by employing a 3-D reference object. That is, a number of plane object images (POIs) computationally reconstructed from the 3-D reference object are used for the 3-D volumetric target recognition. In other words, simultaneous 3-D image correlations between two sets of target and reference POIs, which are depth-dependently reconstructed by using the CIIR method, are performed for effective recognition of 3-D volumetric objects in the proposed system. Successful experiments with this CIIR-based 3-D image correlator confirmed the feasibility of the proposed method.

  15. Object Recognition with Severe Spatial Deficits in Williams Syndrome: Sparing and Breakdown

    ERIC Educational Resources Information Center

    Landau, Barbara; Hoffman, James E.; Kurz, Nicole

    2006-01-01

    Williams syndrome (WS) is a rare genetic disorder that results in severe visual-spatial cognitive deficits coupled with relative sparing in language, face recognition, and certain aspects of motion processing. Here, we look for evidence for sparing or impairment in another cognitive system--object recognition. Children with WS, normal mental-age…

  16. Spatiotemporal information during unsupervised learning enhances viewpoint invariant object recognition.

    PubMed

    Tian, Moqian; Grill-Spector, Kalanit

    2015-01-01

    Recognizing objects is difficult because it requires both linking views of an object that can be different and distinguishing objects with similar appearance. Interestingly, people can learn to recognize objects across views in an unsupervised way, without feedback, just from the natural viewing statistics. However, there is intense debate regarding what information during unsupervised learning is used to link among object views. Specifically, researchers argue whether temporal proximity, motion, or spatiotemporal continuity among object views during unsupervised learning is beneficial. Here, we untangled the role of each of these factors in unsupervised learning of novel three-dimensional (3-D) objects. We found that after unsupervised training with 24 object views spanning a 180° view space, participants showed significant improvement in their ability to recognize 3-D objects across rotation. Surprisingly, there was no advantage to unsupervised learning with spatiotemporal continuity or motion information than training with temporal proximity. However, we discovered that when participants were trained with just a third of the views spanning the same view space, unsupervised learning via spatiotemporal continuity yielded significantly better recognition performance on novel views than learning via temporal proximity. These results suggest that while it is possible to obtain view-invariant recognition just from observing many views of an object presented in temporal proximity, spatiotemporal information enhances performance by producing representations with broader view tuning than learning via temporal association. Our findings have important implications for theories of object recognition and for the development of computational algorithms that learn from examples.

  17. Sensor agnostic object recognition using a map seeking circuit

    NASA Astrophysics Data System (ADS)

    Overman, Timothy L.; Hart, Michael

    2012-05-01

    Automatic object recognition capabilities are traditionally tuned to exploit the specific sensing modality they were designed to. Their successes (and shortcomings) are tied to object segmentation from the background, they typically require highly skilled personnel to train them, and they become cumbersome with the introduction of new objects. In this paper we describe a sensor independent algorithm based on the biologically inspired technology of map seeking circuits (MSC) which overcomes many of these obstacles. In particular, the MSC concept offers transparency in object recognition from a common interface to all sensor types, analogous to a USB device. It also provides a common core framework that is independent of the sensor and expandable to support high dimensionality decision spaces. Ease in training is assured by using commercially available 3D models from the video game community. The search time remains linear no matter how many objects are introduced, ensuring rapid object recognition. Here, we report results of an MSC algorithm applied to object recognition and pose estimation from high range resolution radar (1D), electrooptical imagery (2D), and LIDAR point clouds (3D) separately. By abstracting the sensor phenomenology from the underlying a prior knowledge base, MSC shows promise as an easily adaptable tool for incorporating additional sensor inputs.

  18. Improving human object recognition performance using video enhancement techniques

    NASA Astrophysics Data System (ADS)

    Whitman, Lucy S.; Lewis, Colin; Oakley, John P.

    2004-12-01

    Atmospheric scattering causes significant degradation in the quality of video images, particularly when imaging over long distances. The principle problem is the reduction in contrast due to scattered light. It is known that when the scattering particles are not too large compared with the imaging wavelength (i.e. Mie scattering) then high spatial resolution information may be contained within a low-contrast image. Unfortunately this information is not easily perceived by a human observer, particularly when using a standard video monitor. A secondary problem is the difficulty of achieving a sharp focus since automatic focus techniques tend to fail in such conditions. Recently several commercial colour video processing systems have become available. These systems use various techniques to improve image quality in low contrast conditions whilst retaining colour content. These systems produce improvements in subjective image quality in some situations, particularly in conditions of haze and light fog. There is also some evidence that video enhancement leads to improved ATR performance when used as a pre-processing stage. Psychological literature indicates that low contrast levels generally lead to a reduction in the performance of human observers in carrying out simple visual tasks. The aim of this paper is to present the results of an empirical study on object recognition in adverse viewing conditions. The chosen visual task was vehicle number plate recognition at long ranges (500 m and beyond). Two different commercial video enhancement systems are evaluated using the same protocol. The results show an increase in effective range with some differences between the different enhancement systems.

  19. Object recognition and pose estimation of planar objects from range data

    NASA Technical Reports Server (NTRS)

    Pendleton, Thomas W.; Chien, Chiun Hong; Littlefield, Mark L.; Magee, Michael

    1994-01-01

    The Extravehicular Activity Helper/Retriever (EVAHR) is a robotic device currently under development at the NASA Johnson Space Center that is designed to fetch objects or to assist in retrieving an astronaut who may have become inadvertently de-tethered. The EVAHR will be required to exhibit a high degree of intelligent autonomous operation and will base much of its reasoning upon information obtained from one or more three-dimensional sensors that it will carry and control. At the highest level of visual cognition and reasoning, the EVAHR will be required to detect objects, recognize them, and estimate their spatial orientation and location. The recognition phase and estimation of spatial pose will depend on the ability of the vision system to reliably extract geometric features of the objects such as whether the surface topologies observed are planar or curved and the spatial relationships between the component surfaces. In order to achieve these tasks, three-dimensional sensing of the operational environment and objects in the environment will therefore be essential. One of the sensors being considered to provide image data for object recognition and pose estimation is a phase-shift laser scanner. The characteristics of the data provided by this scanner have been studied and algorithms have been developed for segmenting range images into planar surfaces, extracting basic features such as surface area, and recognizing the object based on the characteristics of extracted features. Also, an approach has been developed for estimating the spatial orientation and location of the recognized object based on orientations of extracted planes and their intersection points. This paper presents some of the algorithms that have been developed for the purpose of recognizing and estimating the pose of objects as viewed by the laser scanner, and characterizes the desirability and utility of these algorithms within the context of the scanner itself, considering data quality and

  20. Hypnotizability and haptics: visual recognition of unimanually explored 'nonmeaningful' objects.

    PubMed

    Castellani, E; Carli, G; Santarcangelo, E L

    2012-08-01

    The cognitive trait of hypnotizability modulates sensorimotor integration and mental imagery. In particular, earlier results show that visual recognition of 'nonmeaningful', unfamiliar objects bimanually explored is faster and more accurate in subjects with high (Highs) than with low hypnotizability (Lows). The present study was aimed at investigating whether Highs exhibit a similar advantage after unimanual exploration. Recognition frequency (RF) and Recognition time (RT) of correct recognitions of the explored objects were recorded. The results showed the absence of any hypnotizability-related difference in recognition frequencies. In addition, RF of the right and left hand was comparable in Highs as in Lows, while slight differences were found in RT. We suggest that hemispheric co-operation played a key role in the better performance of Highs in the bimanual task previously studied. In the unimanual exploration, the task's characteristics (favoring the left hand), hypnotizability-related cerebral asymmetry (favoring the right hand in Highs) and the possible preferential verbal style of recognition in Lows (favoring the right hand in this group) antagonize each other and prevent the occurrence of major differences between the performance of Highs and Lows.

  1. Neural representation for object recognition in inferotemporal cortex.

    PubMed

    Lehky, Sidney R; Tanaka, Keiji

    2016-04-01

    We suggest that population representation of objects in inferotemporal cortex lie on a continuum between a purely structural, parts-based description and a purely holistic description. The intrinsic dimensionality of object representation is estimated to be around 100, perhaps with lower dimensionalities for object representations more toward the holistic end of the spectrum. Cognitive knowledge in the form of semantic information and task information feed back to inferotemporal cortex from perirhinal and prefrontal cortex respectively, providing high-level multimodal-based expectations that assist in the interpretation of object stimuli. Integration of object information across eye movements may also contribute to object recognition through a process of active vision. PMID:26771242

  2. 3-D object recognition using 2-D views.

    PubMed

    Li, Wenjing; Bebis, George; Bourbakis, Nikolaos G

    2008-11-01

    We consider the problem of recognizing 3-D objects from 2-D images using geometric models and assuming different viewing angles and positions. Our goal is to recognize and localize instances of specific objects (i.e., model-based) in a scene. This is in contrast to category-based object recognition methods where the goal is to search for instances of objects that belong to a certain visual category (e.g., faces or cars). The key contribution of our work is improving 3-D object recognition by integrating Algebraic Functions of Views (AFoVs), a powerful framework for predicting the geometric appearance of an object due to viewpoint changes, with indexing and learning. During training, we compute the space of views that groups of object features can produce under the assumption of 3-D linear transformations, by combining a small number of reference views that contain the object features using AFoVs. Unrealistic views (e.g., due to the assumption of 3-D linear transformations) are eliminated by imposing a pair of rigidity constraints based on knowledge of the transformation between the reference views of the object. To represent the space of views that an object can produce compactly while allowing efficient hypothesis generation during recognition, we propose combining indexing with learning in two stages. In the first stage, we sample the space of views of an object sparsely and represent information about the samples using indexing. In the second stage, we build probabilistic models of shape appearance by sampling the space of views of the object densely and learning the manifold formed by the samples. Learning employs the Expectation-Maximization (EM) algorithm and takes place in a "universal," lower-dimensional, space computed through Random Projection (RP). During recognition, we extract groups of point features from the scene and we use indexing to retrieve the most feasible model groups that might have produced them (i.e., hypothesis generation). The likelihood

  3. What are the Visual Features Underlying Rapid Object Recognition?

    PubMed Central

    Crouzet, Sébastien M.; Serre, Thomas

    2011-01-01

    Research progress in machine vision has been very significant in recent years. Robust face detection and identification algorithms are already readily available to consumers, and modern computer vision algorithms for generic object recognition are now coping with the richness and complexity of natural visual scenes. Unlike early vision models of object recognition that emphasized the role of figure-ground segmentation and spatial information between parts, recent successful approaches are based on the computation of loose collections of image features without prior segmentation or any explicit encoding of spatial relations. While these models remain simplistic models of visual processing, they suggest that, in principle, bottom-up activation of a loose collection of image features could support the rapid recognition of natural object categories and provide an initial coarse visual representation before more complex visual routines and attentional mechanisms take place. Focusing on biologically plausible computational models of (bottom-up) pre-attentive visual recognition, we review some of the key visual features that have been described in the literature. We discuss the consistency of these feature-based representations with classical theories from visual psychology and test their ability to account for human performance on a rapid object categorization task. PMID:22110461

  4. Speckle-learning-based object recognition through scattering media.

    PubMed

    Ando, Takamasa; Horisaki, Ryoichi; Tanida, Jun

    2015-12-28

    We experimentally demonstrated object recognition through scattering media based on direct machine learning of a number of speckle intensity images. In the experiments, speckle intensity images of amplitude or phase objects on a spatial light modulator between scattering plates were captured by a camera. We used the support vector machine for binary classification of the captured speckle intensity images of face and non-face data. The experimental results showed that speckles are sufficient for machine learning. PMID:26832049

  5. Nicotine Administration Attenuates Methamphetamine-Induced Novel Object Recognition Deficits

    PubMed Central

    Vieira-Brock, Paula L.; McFadden, Lisa M.; Nielsen, Shannon M.; Smith, Misty D.; Hanson, Glen R.

    2015-01-01

    Background: Previous studies have demonstrated that methamphetamine abuse leads to memory deficits and these are associated with relapse. Furthermore, extensive evidence indicates that nicotine prevents and/or improves memory deficits in different models of cognitive dysfunction and these nicotinic effects might be mediated by hippocampal or cortical nicotinic acetylcholine receptors. The present study investigated whether nicotine attenuates methamphetamine-induced novel object recognition deficits in rats and explored potential underlying mechanisms. Methods: Adolescent or adult male Sprague-Dawley rats received either nicotine water (10–75 μg/mL) or tap water for several weeks. Methamphetamine (4×7.5mg/kg/injection) or saline was administered either before or after chronic nicotine exposure. Novel object recognition was evaluated 6 days after methamphetamine or saline. Serotonin transporter function and density and α4β2 nicotinic acetylcholine receptor density were assessed on the following day. Results: Chronic nicotine intake via drinking water beginning during either adolescence or adulthood attenuated the novel object recognition deficits caused by a high-dose methamphetamine administration. Similarly, nicotine attenuated methamphetamine-induced deficits in novel object recognition when administered after methamphetamine treatment. However, nicotine did not attenuate the serotonergic deficits caused by methamphetamine in adults. Conversely, nicotine attenuated methamphetamine-induced deficits in α4β2 nicotinic acetylcholine receptor density in the hippocampal CA1 region. Furthermore, nicotine increased α4β2 nicotinic acetylcholine receptor density in the hippocampal CA3, dentate gyrus and perirhinal cortex in both saline- and methamphetamine-treated rats. Conclusions: Overall, these findings suggest that nicotine-induced increases in α4β2 nicotinic acetylcholine receptors in the hippocampus and perirhinal cortex might be one mechanism by which

  6. Invariant visual object recognition and shape processing in rats

    PubMed Central

    Zoccolan, Davide

    2015-01-01

    Invariant visual object recognition is the ability to recognize visual objects despite the vastly different images that each object can project onto the retina during natural vision, depending on its position and size within the visual field, its orientation relative to the viewer, etc. Achieving invariant recognition represents such a formidable computational challenge that is often assumed to be a unique hallmark of primate vision. Historically, this has limited the invasive investigation of its neuronal underpinnings to monkey studies, in spite of the narrow range of experimental approaches that these animal models allow. Meanwhile, rodents have been largely neglected as models of object vision, because of the widespread belief that they are incapable of advanced visual processing. However, the powerful array of experimental tools that have been developed to dissect neuronal circuits in rodents has made these species very attractive to vision scientists too, promoting a new tide of studies that have started to systematically explore visual functions in rats and mice. Rats, in particular, have been the subjects of several behavioral studies, aimed at assessing how advanced object recognition and shape processing is in this species. Here, I review these recent investigations, as well as earlier studies of rat pattern vision, to provide an historical overview and a critical summary of the status of the knowledge about rat object vision. The picture emerging from this survey is very encouraging with regard to the possibility of using rats as complementary models to monkeys in the study of higher-level vision. PMID:25561421

  7. Priming for novel object associations: Neural differences from object item priming and equivalent forms of recognition.

    PubMed

    Gomes, Carlos Alexandre; Figueiredo, Patrícia; Mayes, Andrew

    2016-04-01

    The neural substrates of associative and item priming and recognition were investigated in a functional magnetic resonance imaging study over two separate sessions. In the priming session, participants decided which object of a pair was bigger during both study and test phases. In the recognition session, participants saw different object pairs and performed the same size-judgement task followed by an associative recognition memory task. Associative priming was accompanied by reduced activity in the right middle occipital gyrus as well as in bilateral hippocampus. Object item priming was accompanied by reduced activity in extensive priming-related areas in the bilateral occipitotemporofrontal cortex, as well as in the perirhinal cortex, but not in the hippocampus. Associative recognition was characterized by activity increases in regions linked to recollection, such as the hippocampus, posterior cingulate cortex, anterior medial frontal gyrus and posterior parahippocampal cortex. Item object priming and recognition recruited broadly overlapping regions (e.g., bilateral middle occipital and prefrontal cortices, left fusiform gyrus), even though the BOLD response was in opposite directions. These regions along with the precuneus, where both item priming and recognition were accompanied by activation, have been found to respond to object familiarity. The minimal structural overlap between object associative priming and recollection-based associative recognition suggests that they depend on largely different stimulus-related information and that the different directions of the effects indicate distinct retrieval mechanisms. In contrast, item priming and familiarity-based recognition seemed mainly based on common memory information, although the extent of common processing between priming and familiarity remains unclear. Further implications of these findings are discussed. PMID:26418396

  8. Multispectral image analysis for object recognition and classification

    NASA Astrophysics Data System (ADS)

    Viau, C. R.; Payeur, P.; Cretu, A.-M.

    2016-05-01

    Computer and machine vision applications are used in numerous fields to analyze static and dynamic imagery in order to assist or automate decision-making processes. Advancements in sensor technologies now make it possible to capture and visualize imagery at various wavelengths (or bands) of the electromagnetic spectrum. Multispectral imaging has countless applications in various fields including (but not limited to) security, defense, space, medical, manufacturing and archeology. The development of advanced algorithms to process and extract salient information from the imagery is a critical component of the overall system performance. The fundamental objective of this research project was to investigate the benefits of combining imagery from the visual and thermal bands of the electromagnetic spectrum to improve the recognition rates and accuracy of commonly found objects in an office setting. A multispectral dataset (visual and thermal) was captured and features from the visual and thermal images were extracted and used to train support vector machine (SVM) classifiers. The SVM's class prediction ability was evaluated separately on the visual, thermal and multispectral testing datasets.

  9. Biological object recognition in μ-radiography images

    NASA Astrophysics Data System (ADS)

    Prochazka, A.; Dammer, J.; Weyda, F.; Sopko, V.; Benes, J.; Zeman, J.; Jandejsek, I.

    2015-03-01

    This study presents an applicability of real-time microradiography to biological objects, namely to horse chestnut leafminer, Cameraria ohridella (Insecta: Lepidoptera, Gracillariidae) and following image processing focusing on image segmentation and object recognition. The microradiography of insects (such as horse chestnut leafminer) provides a non-invasive imaging that leaves the organisms alive. The imaging requires a high spatial resolution (micrometer scale) radiographic system. Our radiographic system consists of a micro-focus X-ray tube and two types of detectors. The first is a charge integrating detector (Hamamatsu flat panel), the second is a pixel semiconductor detector (Medipix2 detector). The latter allows detection of single quantum photon of ionizing radiation. We obtained numerous horse chestnuts leafminer pupae in several microradiography images easy recognizable in automatic mode using the image processing methods. We implemented an algorithm that is able to count a number of dead and alive pupae in images. The algorithm was based on two methods: 1) noise reduction using mathematical morphology filters, 2) Canny edge detection. The accuracy of the algorithm is higher for the Medipix2 (average recall for detection of alive pupae =0.99, average recall for detection of dead pupae =0.83), than for the flat panel (average recall for detection of alive pupae =0.99, average recall for detection of dead pupae =0.77). Therefore, we conclude that Medipix2 has lower noise and better displays contours (edges) of biological objects. Our method allows automatic selection and calculation of dead and alive chestnut leafminer pupae. It leads to faster monitoring of the population of one of the world's important insect pest.

  10. Comparison of Object Recognition Behavior in Human and Monkey

    PubMed Central

    Rajalingham, Rishi; Schmidt, Kailyn

    2015-01-01

    Although the rhesus monkey is used widely as an animal model of human visual processing, it is not known whether invariant visual object recognition behavior is quantitatively comparable across monkeys and humans. To address this question, we systematically compared the core object recognition behavior of two monkeys with that of human subjects. To test true object recognition behavior (rather than image matching), we generated several thousand naturalistic synthetic images of 24 basic-level objects with high variation in viewing parameters and image background. Monkeys were trained to perform binary object recognition tasks on a match-to-sample paradigm. Data from 605 human subjects performing the same tasks on Mechanical Turk were aggregated to characterize “pooled human” object recognition behavior, as well as 33 separate Mechanical Turk subjects to characterize individual human subject behavior. Our results show that monkeys learn each new object in a few days, after which they not only match mean human performance but show a pattern of object confusion that is highly correlated with pooled human confusion patterns and is statistically indistinguishable from individual human subjects. Importantly, this shared human and monkey pattern of 3D object confusion is not shared with low-level visual representations (pixels, V1+; models of the retina and primary visual cortex) but is shared with a state-of-the-art computer vision feature representation. Together, these results are consistent with the hypothesis that rhesus monkeys and humans share a common neural shape representation that directly supports object perception. SIGNIFICANCE STATEMENT To date, several mammalian species have shown promise as animal models for studying the neural mechanisms underlying high-level visual processing in humans. In light of this diversity, making tight comparisons between nonhuman and human primates is particularly critical in determining the best use of nonhuman primates to

  11. Superior voice recognition in a patient with acquired prosopagnosia and object agnosia.

    PubMed

    Hoover, Adria E N; Démonet, Jean-François; Steeves, Jennifer K E

    2010-11-01

    Anecdotally, it has been reported that individuals with acquired prosopagnosia compensate for their inability to recognize faces by using other person identity cues such as hair, gait or the voice. Are they therefore superior at the use of non-face cues, specifically voices, to person identity? Here, we empirically measure person and object identity recognition in a patient with acquired prosopagnosia and object agnosia. We quantify person identity (face and voice) and object identity (car and horn) recognition for visual, auditory, and bimodal (visual and auditory) stimuli. The patient is unable to recognize faces or cars, consistent with his prosopagnosia and object agnosia, respectively. He is perfectly able to recognize people's voices and car horns and bimodal stimuli. These data show a reverse shift in the typical weighting of visual over auditory information for audiovisual stimuli in a compromised visual recognition system. Moreover, the patient shows selectively superior voice recognition compared to the controls revealing that two different stimulus domains, persons and objects, may not be equally affected by sensory adaptation effects. This also implies that person and object identity recognition are processed in separate pathways. These data demonstrate that an individual with acquired prosopagnosia and object agnosia can compensate for the visual impairment and become quite skilled at using spared aspects of sensory processing. In the case of acquired prosopagnosia it is advantageous to develop a superior use of voices for person identity recognition in everyday life.

  12. Biologically Motivated Novel Localization Paradigm by High-Level Multiple Object Recognition in Panoramic Images

    PubMed Central

    Kim, Sungho; Shim, Min-Sheob

    2015-01-01

    This paper presents the novel paradigm of a global localization method motivated by human visual systems (HVSs). HVSs actively use the information of the object recognition results for self-position localization and for viewing direction. The proposed localization paradigm consisted of three parts: panoramic image acquisition, multiple object recognition, and grid-based localization. Multiple object recognition information from panoramic images is utilized in the localization part. High-level object information was useful not only for global localization, but also for robot-object interactions. The metric global localization (position, viewing direction) was conducted based on the bearing information of recognized objects from just one panoramic image. The feasibility of the novel localization paradigm was validated experimentally. PMID:26457323

  13. Object Locating System

    NASA Technical Reports Server (NTRS)

    Arndt, G. Dickey (Inventor); Carl, James R. (Inventor)

    2000-01-01

    A portable system is provided that is operational for determining, with three dimensional resolution, the position of a buried object or approximately positioned object that may move in space or air or gas. The system has a plurality of receivers for detecting the signal front a target antenna and measuring the phase thereof with respect to a reference signal. The relative permittivity and conductivity of the medium in which the object is located is used along with the measured phase signal to determine a distance between the object and each of the plurality of receivers. Knowing these distances. an iteration technique is provided for solving equations simultaneously to provide position coordinates. The system may also be used for tracking movement of an object within close range of the system by sampling and recording subsequent position of the object. A dipole target antenna. when positioned adjacent to a buried object, may be energized using a separate transmitter which couples energy to the target antenna through the medium. The target antenna then preferably resonates at a different frequency, such as a second harmonic of the transmitter frequency.

  14. Trajectory Recognition as the Basis for Object Individuation: A Functional Model of Object File Instantiation and Object-Token Encoding

    PubMed Central

    Fields, Chris

    2011-01-01

    The perception of persisting visual objects is mediated by transient intermediate representations, object files, that are instantiated in response to some, but not all, visual trajectories. The standard object file concept does not, however, provide a mechanism sufficient to account for all experimental data on visual object persistence, object tracking, and the ability to perceive spatially disconnected stimuli as continuously existing objects. Based on relevant anatomical, functional, and developmental data, a functional model is constructed that bases visual object individuation on the recognition of temporal sequences of apparent center-of-mass positions that are specifically identified as trajectories by dedicated “trajectory recognition networks” downstream of the medial–temporal motion-detection area. This model is shown to account for a wide range of data, and to generate a variety of testable predictions. Individual differences in the recognition, abstraction, and encoding of trajectory information are expected to generate distinct object persistence judgments and object recognition abilities. Dominance of trajectory information over feature information in stored object tokens during early infancy, in particular, is expected to disrupt the ability to re-identify human and other individuals across perceptual episodes, and lead to developmental outcomes with characteristics of autism spectrum disorders. PMID:21716599

  15. Atypical Time Course of Object Recognition in Autism Spectrum Disorder

    PubMed Central

    Caplette, Laurent; Wicker, Bruno; Gosselin, Frédéric

    2016-01-01

    In neurotypical observers, it is widely believed that the visual system samples the world in a coarse-to-fine fashion. Past studies on Autism Spectrum Disorder (ASD) have identified atypical responses to fine visual information but did not investigate the time course of the sampling of information at different levels of granularity (i.e. Spatial Frequencies, SF). Here, we examined this question during an object recognition task in ASD and neurotypical observers using a novel experimental paradigm. Our results confirm and characterize with unprecedented precision a coarse-to-fine sampling of SF information in neurotypical observers. In ASD observers, we discovered a different pattern of SF sampling across time: in the first 80 ms, high SFs lead ASD observers to a higher accuracy than neurotypical observers, and these SFs are sampled differently across time in the two subject groups. Our results might be related to the absence of a mandatory precedence of global information, and to top-down processing abnormalities in ASD. PMID:27752088

  16. The relationship between protein synthesis and protein degradation in object recognition memory.

    PubMed

    Furini, Cristiane R G; Myskiw, Jociane de C; Schmidt, Bianca E; Zinn, Carolina G; Peixoto, Patricia B; Pereira, Luiza D; Izquierdo, Ivan

    2015-11-01

    For decades there has been a consensus that de novo protein synthesis is necessary for long-term memory. A second round of protein synthesis has been described for both extinction and reconsolidation following an unreinforced test session. Recently, it was shown that consolidation and reconsolidation depend not only on protein synthesis but also on protein degradation by the ubiquitin-proteasome system (UPS), a major mechanism responsible for protein turnover. However, the involvement of UPS on consolidation and reconsolidation of object recognition memory remains unknown. Here we investigate in the CA1 region of the dorsal hippocampus the involvement of UPS-mediated protein degradation in consolidation and reconsolidation of object recognition memory. Animals with infusion cannulae stereotaxically implanted in the CA1 region of the dorsal hippocampus, were exposed to an object recognition task. The UPS inhibitor β-Lactacystin did not affect the consolidation and the reconsolidation of object recognition memory at doses known to affect other forms of memory (inhibitory avoidance, spatial learning in a water maze) while the protein synthesis inhibitor anisomycin impaired the consolidation and the reconsolidation of the object recognition memory. However, β-Lactacystin was able to reverse the impairment caused by anisomycin on the reconsolidation process in the CA1 region of the hippocampus. Therefore, it is possible to postulate a direct link between protein degradation and protein synthesis during the reconsolidation of the object recognition memory.

  17. Asymptotic analysis of pattern-theoretic object recognition

    NASA Astrophysics Data System (ADS)

    Cooper, Matthew L.; Srivastava, Anuj

    2000-08-01

    Automated target recognition (ATR) is a problem of great importance in a wide variety of applications: from military target recognition to recognizing flow-patterns in fluid- dynamics to anatomical shape-studies. The basic goal is to utilize observations (images, signals) from remote sensors (such as videos, radars, MRI or PET) to identify the objects being observed. In a statistical framework, probability distributions on parameters representing the object unknowns are derived an analyzed to compute inferences (please refer to [1] for a detailed introduction). An important challenge in ATR is to determine efficient mathematical models for the tremendous variability of object appearance which lend themselves to reasonable inferences. This variation may be due to differences in object shapes, sensor-mechanisms or scene- backgrounds. To build models for object variabilities, we employ deformable templates. In brief, the object occurrences are described through their typical representatives (called templates) and transformations/deformations which particularize the templates to the observed objects. Within this pattern-theoretic framework, ATR becomes a problem of selecting appropriate templates and estimating deformations. For an object (alpha) (epsilon) A, let I(alpha ) denote a template (for example triangulated CAD-surface) and let s (epsilon) S be a particular transformation, then denote the transformed template by sI(alpha ). Figure 1 shows instances of the template for a T62 tank at several different orientations. For the purpose of object classification, the unknown transformation s is considered a nuisance parameter, leading to a classical formulation of Bayesian hypothesis- testing in presence of unknown, random nuisance parameters. S may not be a vector-space, but it often has a group structure. For rigid objects, the variation in translation and rotation can be modeled through the action of special Euclidean group SE(n). For flexible objects, such as

  18. Early recurrent feedback facilitates visual object recognition under challenging conditions

    PubMed Central

    Wyatte, Dean; Jilk, David J.; O'Reilly, Randall C.

    2014-01-01

    Standard models of the visual object recognition pathway hold that a largely feedforward process from the retina through inferotemporal cortex leads to object identification. A subsequent feedback process originating in frontoparietal areas through reciprocal connections to striate cortex provides attentional support to salient or behaviorally-relevant features. Here, we review mounting evidence that feedback signals also originate within extrastriate regions and begin during the initial feedforward process. This feedback process is temporally dissociable from attention and provides important functions such as grouping, associational reinforcement, and filling-in of features. Local feedback signals operating concurrently with feedforward processing are important for object identification in noisy real-world situations, particularly when objects are partially occluded, unclear, or otherwise ambiguous. Altogether, the dissociation of early and late feedback processes presented here expands on current models of object identification, and suggests a dual role for descending feedback projections. PMID:25071647

  19. How does the brain solve visual object recognition?

    PubMed Central

    Zoccolan, Davide; Rust, Nicole C.

    2012-01-01

    Mounting evidence suggests that “core object recognition,” the ability to rapidly recognize objects despite substantial appearance variation, is solved in the brain via a cascade of reflexive, largely feedforward computations that culminate in a powerful neuronal representation in the inferior temporal cortex. However, the algorithm that produces this solution remains little-understood. Here we review evidence ranging from individual neurons, to neuronal populations, to behavior, to computational models. We propose that understanding this algorithm will require using neuronal and psychophysical data to sift through many computational models, each based on building blocks of small, canonical sub-networks with a common functional goal. PMID:22325196

  20. Affective and contextual values modulate spatial frequency use in object recognition

    PubMed Central

    Caplette, Laurent; West, Gregory; Gomot, Marie; Gosselin, Frédéric; Wicker, Bruno

    2014-01-01

    Visual object recognition is of fundamental importance in our everyday interaction with the environment. Recent models of visual perception emphasize the role of top-down predictions facilitating object recognition via initial guesses that limit the number of object representations that need to be considered. Several results suggest that this rapid and efficient object processing relies on the early extraction and processing of low spatial frequencies (LSF). The present study aimed to investigate the SF content of visual object representations and its modulation by contextual and affective values of the perceived object during a picture-name verification task. Stimuli consisted of pictures of objects equalized in SF content and categorized as having low or high affective and contextual values. To access the SF content of stored visual representations of objects, SFs of each image were then randomly sampled on a trial-by-trial basis. Results reveal that intermediate SFs between 14 and 24 cycles per object (2.3–4 cycles per degree) are correlated with fast and accurate identification for all categories of objects. Moreover, there was a significant interaction between affective and contextual values over the SFs correlating with fast recognition. These results suggest that affective and contextual values of a visual object modulate the SF content of its internal representation, thus highlighting the flexibility of the visual recognition system. PMID:24904514

  1. Neural network application for thermal image recognition of low-resolution objects

    NASA Astrophysics Data System (ADS)

    Fang, Yi-Chin; Wu, Bo-Wen

    2007-02-01

    In the ever-changing situation on a battle field, accurate recognition of a distant object is critical to a commander's decision-making and the general public's safety. Efficiently distinguishing between an enemy's armoured vehicles and ordinary civilian houses under all weather conditions has become an important research topic. This study presents a system for recognizing an armoured vehicle by distinguishing marks and contours. The characteristics of 12 different shapes and 12 characters are used to explore thermal image recognition under the circumstance of long distance and low resolution. Although the recognition capability of human eyes is superior to that of artificial intelligence under normal conditions, it tends to deteriorate substantially under long-distance and low-resolution scenarios. This study presents an effective method for choosing features and processing images. The artificial neural network technique is applied to further improve the probability of accurate recognition well beyond the limit of the recognition capability of human eyes.

  2. Methylphenidate restores novel object recognition in DARPP-32 knockout mice.

    PubMed

    Heyser, Charles J; McNaughton, Caitlyn H; Vishnevetsky, Donna; Fienberg, Allen A

    2013-09-15

    Previously, we have shown that Dopamine- and cAMP-regulated phosphoprotein of 32kDa (DARPP-32) knockout mice required significantly more trials to reach criterion than wild-type mice in an operant reversal-learning task. The present study was conducted to examine adult male and female DARPP-32 knockout mice and wild-type controls in a novel object recognition test. Wild-type and knockout mice exhibited comparable behavior during the initial exploration trials. As expected, wild-type mice exhibited preferential exploration of the novel object during the substitution test, demonstrating recognition memory. In contrast, knockout mice did not show preferential exploration of the novel object, instead exhibiting an increase in exploration of all objects during the test trial. Given that the removal of DARPP-32 is an intracellular manipulation, it seemed possible to pharmacologically restore some cellular activity and behavior by stimulating dopamine receptors. Therefore, a second experiment was conducted examining the effect of methylphenidate. The results show that methylphenidate increased horizontal activity in both wild-type and knockout mice, though this increase was blunted in knockout mice. Pretreatment with methylphenidate significantly impaired novel object recognition in wild-type mice. In contrast, pretreatment with methylphenidate restored the behavior of DARPP-32 knockout mice to that observed in wild-type mice given saline. These results provide additional evidence for a functional role of DARPP-32 in the mediation of processes underlying learning and memory. These results also indicate that the behavioral deficits in DARPP-32 knockout mice may be restored by the administration of methylphenidate.

  3. Objective 3D face recognition: Evolution, approaches and challenges.

    PubMed

    Smeets, Dirk; Claes, Peter; Vandermeulen, Dirk; Clement, John Gerald

    2010-09-10

    Face recognition is a natural human ability and a widely accepted identification and authentication method. In modern legal settings, a lot of credence is placed on identifications made by eyewitnesses. Consequently these are based on human perception which is often flawed and can lead to situations where identity is disputed. Therefore, there is a clear need to secure identifications in an objective way based on anthropometric measures. Anthropometry has existed for many years and has evolved with each advent of new technology and computing power. As a result of this, face recognition methodology has shifted from a purely 2D image-based approach to the use of 3D facial shape. However, one of the main challenges still remaining is the non-rigid structure of the face, which can change permanently over varying time-scales and briefly with facial expressions. The majority of face recognition methods have been developed by scientists with a very technical background such as biometry, pattern recognition and computer vision. This article strives to bridge the gap between these communities and the forensic science end-users. A concise review of face recognition using 3D shape is given. Methods using 3D shape applied to data embodying facial expressions are tabulated for reference. From this list a categorization of different strategies to deal with expressions is presented. The underlying concepts and practical issues relating to the application of each strategy are given, without going into technical details. The discussion clearly articulates the justification to establish archival, reference databases to compare and evaluate different strategies. PMID:20395086

  4. Objective 3D face recognition: Evolution, approaches and challenges.

    PubMed

    Smeets, Dirk; Claes, Peter; Vandermeulen, Dirk; Clement, John Gerald

    2010-09-10

    Face recognition is a natural human ability and a widely accepted identification and authentication method. In modern legal settings, a lot of credence is placed on identifications made by eyewitnesses. Consequently these are based on human perception which is often flawed and can lead to situations where identity is disputed. Therefore, there is a clear need to secure identifications in an objective way based on anthropometric measures. Anthropometry has existed for many years and has evolved with each advent of new technology and computing power. As a result of this, face recognition methodology has shifted from a purely 2D image-based approach to the use of 3D facial shape. However, one of the main challenges still remaining is the non-rigid structure of the face, which can change permanently over varying time-scales and briefly with facial expressions. The majority of face recognition methods have been developed by scientists with a very technical background such as biometry, pattern recognition and computer vision. This article strives to bridge the gap between these communities and the forensic science end-users. A concise review of face recognition using 3D shape is given. Methods using 3D shape applied to data embodying facial expressions are tabulated for reference. From this list a categorization of different strategies to deal with expressions is presented. The underlying concepts and practical issues relating to the application of each strategy are given, without going into technical details. The discussion clearly articulates the justification to establish archival, reference databases to compare and evaluate different strategies.

  5. Touching and Hearing Unseen Objects: Multisensory Effects on Scene Recognition

    PubMed Central

    van Lier, Rob

    2016-01-01

    In three experiments, we investigated the influence of object-specific sounds on haptic scene recognition without vision. Blindfolded participants had to recognize, through touch, spatial scenes comprising six objects that were placed on a round platform. Critically, in half of the trials, object-specific sounds were played when objects were touched (bimodal condition), while sounds were turned off in the other half of the trials (unimodal condition). After first exploring the scene, two objects were swapped and the task was to report, which of the objects swapped positions. In Experiment 1, geometrical objects and simple sounds were used, while in Experiment 2, the objects comprised toy animals that were matched with semantically compatible animal sounds. In Experiment 3, we replicated Experiment 1, but now a tactile-auditory object identification task preceded the experiment in which the participants learned to identify the objects based on tactile and auditory input. For each experiment, the results revealed a significant performance increase only after the switch from bimodal to unimodal. Thus, it appears that the release of bimodal identification, from audio-tactile to tactile-only produces a benefit that is not achieved when having the reversed order in which sound was added after having experience with haptic-only. We conclude that task-related factors other than mere bimodal identification cause the facilitation when switching from bimodal to unimodal conditions. PMID:27698985

  6. Visual appearance interacts with conceptual knowledge in object recognition

    PubMed Central

    Cheung, Olivia S.; Gauthier, Isabel

    2014-01-01

    Objects contain rich visual and conceptual information, but do these two types of information interact? Here, we examine whether visual and conceptual information interact when observers see novel objects for the first time. We then address how this interaction influences the acquisition of perceptual expertise. We used two types of novel objects (Greebles), designed to resemble either animals or tools, and two lists of words, which described non-visual attributes of people or man-made objects. Participants first judged if a word was more suitable for describing people or objects while ignoring a task-irrelevant image, and showed faster responses if the words and the unfamiliar objects were congruent in terms of animacy (e.g., animal-like objects with words that described human). Participants then learned to associate objects and words that were either congruent or not in animacy, before receiving expertise training to rapidly individuate the objects. Congruent pairing of visual and conceptual information facilitated observers' ability to become a perceptual expert, as revealed in a matching task that required visual identification at the basic or subordinate levels. Taken together, these findings show that visual and conceptual information interact at multiple levels in object recognition. PMID:25120509

  7. Touching and Hearing Unseen Objects: Multisensory Effects on Scene Recognition

    PubMed Central

    van Lier, Rob

    2016-01-01

    In three experiments, we investigated the influence of object-specific sounds on haptic scene recognition without vision. Blindfolded participants had to recognize, through touch, spatial scenes comprising six objects that were placed on a round platform. Critically, in half of the trials, object-specific sounds were played when objects were touched (bimodal condition), while sounds were turned off in the other half of the trials (unimodal condition). After first exploring the scene, two objects were swapped and the task was to report, which of the objects swapped positions. In Experiment 1, geometrical objects and simple sounds were used, while in Experiment 2, the objects comprised toy animals that were matched with semantically compatible animal sounds. In Experiment 3, we replicated Experiment 1, but now a tactile-auditory object identification task preceded the experiment in which the participants learned to identify the objects based on tactile and auditory input. For each experiment, the results revealed a significant performance increase only after the switch from bimodal to unimodal. Thus, it appears that the release of bimodal identification, from audio-tactile to tactile-only produces a benefit that is not achieved when having the reversed order in which sound was added after having experience with haptic-only. We conclude that task-related factors other than mere bimodal identification cause the facilitation when switching from bimodal to unimodal conditions.

  8. A chicken model for studying the emergence of invariant object recognition

    PubMed Central

    Wood, Samantha M. W.; Wood, Justin N.

    2015-01-01

    “Invariant object recognition” refers to the ability to recognize objects across variation in their appearance on the retina. This ability is central to visual perception, yet its developmental origins are poorly understood. Traditionally, nonhuman primates, rats, and pigeons have been the most commonly used animal models for studying invariant object recognition. Although these animals have many advantages as model systems, they are not well suited for studying the emergence of invariant object recognition in the newborn brain. Here, we argue that newly hatched chicks (Gallus gallus) are an ideal model system for studying the emergence of invariant object recognition. Using an automated controlled-rearing approach, we show that chicks can build a viewpoint-invariant representation of the first object they see in their life. This invariant representation can be built from highly impoverished visual input (three images of an object separated by 15° azimuth rotations) and cannot be accounted for by low-level retina-like or V1-like neuronal representations. These results indicate that newborn neural circuits begin building invariant object representations at the onset of vision and argue for an increased focus on chicks as an animal model for studying invariant object recognition. PMID:25767436

  9. An audiovisual emotion recognition system

    NASA Astrophysics Data System (ADS)

    Han, Yi; Wang, Guoyin; Yang, Yong; He, Kun

    2007-12-01

    Human emotions could be expressed by many bio-symbols. Speech and facial expression are two of them. They are both regarded as emotional information which is playing an important role in human-computer interaction. Based on our previous studies on emotion recognition, an audiovisual emotion recognition system is developed and represented in this paper. The system is designed for real-time practice, and is guaranteed by some integrated modules. These modules include speech enhancement for eliminating noises, rapid face detection for locating face from background image, example based shape learning for facial feature alignment, and optical flow based tracking algorithm for facial feature tracking. It is known that irrelevant features and high dimensionality of the data can hurt the performance of classifier. Rough set-based feature selection is a good method for dimension reduction. So 13 speech features out of 37 ones and 10 facial features out of 33 ones are selected to represent emotional information, and 52 audiovisual features are selected due to the synchronization when speech and video fused together. The experiment results have demonstrated that this system performs well in real-time practice and has high recognition rate. Our results also show that the work in multimodules fused recognition will become the trend of emotion recognition in the future.

  10. An optical processor for object recognition and tracking

    NASA Technical Reports Server (NTRS)

    Sloan, J.; Udomkesmalee, S.

    1987-01-01

    The design and development of a miniaturized optical processor that performs real time image correlation are described. The optical correlator utilizes the Vander Lugt matched spatial filter technique. The correlation output, a focused beam of light, is imaged onto a CMOS photodetector array. In addition to performing target recognition, the device also tracks the target. The hardware, composed of optical and electro-optical components, occupies only 590 cu cm of volume. A complete correlator system would also include an input imaging lens. This optical processing system is compact, rugged, requires only 3.5 watts of operating power, and weighs less than 3 kg. It represents a major achievement in miniaturizing optical processors. When considered as a special-purpose processing unit, it is an attractive alternative to conventional digital image recognition processing. It is conceivable that the combined technology of both optical and ditital processing could result in a very advanced robot vision system.

  11. Bayesian multi-target tracking and sequential object recognition

    NASA Astrophysics Data System (ADS)

    Armbruster, Walter

    2008-04-01

    The paper considers the following problem: given a 3D model of a reference target and a sequence of images of a 3D scene, identify the object in the scene most likely to be the reference target and determine its current pose. Finding the best match in each frame independently of previous decisions is not optimal, since past information is ignored. Our solution concept uses a novel Bayesian framework for multi target tracking and object recognition to define and sequentially update the probability that the reference target is any one of the tracked objects. The approach is applied to problems of automatic lock-on and missile guidance using a laser radar seeker. Field trials have resulted in high target hit probabilities despite low resolution imagery and temporarily highly occluded targets.

  12. Objective recognition of cough sound as biomarker for aerial pollutants.

    PubMed

    Van Hirtum, A; Berckmans, D

    2004-02-01

    A relationship among air quality, respiratory health, and comfort in man and animal is widely shown. In general, a state of respiratory discomfort is prevailed by an increase in acoustic audible symptoms. The general concept of sound analysis as an objective contactless non-invasive biomarker for aerial pollution is studied on free-field cough sound of 12 Belgian Landrace piglets. A citric-acid-induced cough sound recognition algorithm with recognition rate of 95% is applied to cough sounds registered in the presence of distinct types of aerial pollutants: irritating gas (ammonia), respirable particles (dust), and temperature. The recognition performance for all aerial pollutants was >90% and maintained 94% on average. It is concluded that sound analysis allows an effective biomarker for all three types of aerial pollution. The generality of the biomarker is hypothesized to be due to the common mechanism involved in protective cough. As a consequence, it is suggested to use sound analysis as a biomarker for respiratory state in studies of exposure to air pollutants.

  13. The Army word recognition system

    NASA Technical Reports Server (NTRS)

    Hadden, David R.; Haratz, David

    1977-01-01

    The application of speech recognition technology in the Army command and control area is presented. The problems associated with this program are described as well as as its relevance in terms of the man/machine interactions, voice inflexions, and the amount of training needed to interact with and utilize the automated system.

  14. Object recognition in clutter: cortical responses depend on the type of learning

    PubMed Central

    Hegdé, Jay; Thompson, Serena K.; Brady, Mark; Kersten, Daniel

    2012-01-01

    Theoretical studies suggest that the visual system uses prior knowledge of visual objects to recognize them in visual clutter, and posit that the strategies for recognizing objects in clutter may differ depending on whether or not the object was learned in clutter to begin with. We tested this hypothesis using functional magnetic resonance imaging (fMRI) of human subjects. We trained subjects to recognize naturalistic, yet novel objects in strong or weak clutter. We then tested subjects' recognition performance for both sets of objects in strong clutter. We found many brain regions that were differentially responsive to objects during object recognition depending on whether they were learned in strong or weak clutter. In particular, the responses of the left fusiform gyrus (FG) reliably reflected, on a trial-to-trial basis, subjects' object recognition performance for objects learned in the presence of strong clutter. These results indicate that the visual system does not use a single, general-purpose mechanism to cope with clutter. Instead, there are two distinct spatial patterns of activation whose responses are attributable not to the visual context in which the objects were seen, but to the context in which the objects were learned. PMID:22723774

  15. Auto-associative segmentation for real-time object recognition in realistic outdoor images

    NASA Astrophysics Data System (ADS)

    Estevez, Leonardo W.; Kehtarnavaz, Nasser D.

    1998-04-01

    As digital signal processors (DSPs) become more advanced, many real-time recognition problems will be solved with completely integrated solutions. In this paper a methodology which is designed for today's DSP architectures and is capable of addressing applications in real-time color object recognition is presented. The methodology is integrated into a processing structure called raster scan video processing which requires a small amount of memory. The small amount of memory required enables the entire recognition system to be implemented on a single DSP. This auto-associative segmentation approach provides a means for desaturated color images to be segmented. The system is applied to the problem of stop sign recognition is realistically captured outdoor images.

  16. Estrous cycle, pregnancy, and parity enhance performance of rats in object recognition or object placement tasks

    PubMed Central

    Paris, Jason J; Frye, Cheryl A

    2008-01-01

    Ovarian hormone elevations are associated with enhanced learning/memory. During behavioral estrus or pregnancy, progestins, such as progesterone (P4) and its metabolite 5α-pregnan-3α-ol-20-one (3α,5α-THP), are elevated due, in part, to corpora luteal and placental secretion. During ‘pseudopregnancy’, the induction of corpora luteal functioning results in a hormonal milieu analogous to pregnancy, which ceases after about 12 days, due to the lack of placental formation. Multiparity is also associated with enhanced learning/memory, perhaps due to prior steroid exposure during pregnancy. Given evidence that progestins and/or parity may influence cognition, we investigated how natural alterations in the progestin milieu influence cognitive performance. In Experiment 1, virgin rats (nulliparous) or rats with two prior pregnancies (multiparous) were assessed on the object placement and recognition tasks, when in high-estrogen/P4 (behavioral estrus) or low-estrogen/P4 (diestrus) phases of the estrous cycle. In Experiment 2, primiparous or multiparous rats were tested in the object placement and recognition tasks when not pregnant, pseudopregnant, or pregnant (between gestational days (GDs) 6 and 12). In Experiment 3, pregnant primiparous or multiparous rats were assessed daily in the object placement or recognition tasks. Females in natural states associated with higher endogenous progestins (behavioral estrus, pregnancy, multiparity) outperformed rats in low progestin states (diestrus, non-pregnancy, nulliparity) on the object placement and recognition tasks. In earlier pregnancy, multiparous, compared with primiparous, rats had a lower corticosterone, but higher estrogen levels, concomitant with better object placement performance. From GD 13 until post partum, primiparous rats had higher 3α,5α-THP levels and improved object placement performance compared with multiparous rats. PMID:18390689

  17. Recognition of Simple 3D Geometrical Objects under Partial Occlusion

    NASA Astrophysics Data System (ADS)

    Barchunova, Alexandra; Sommer, Gerald

    In this paper we present a novel procedure for contour-based recognition of partially occluded three-dimensional objects. In our approach we use images of real and rendered objects whose contours have been deformed by a restricted change of the viewpoint. The preparatory part consists of contour extraction, preprocessing, local structure analysis and feature extraction. The main part deals with an extended construction and functionality of the classifier ensemble Adaptive Occlusion Classifier (AOC). It relies on a hierarchical fragmenting algorithm to perform a local structure analysis which is essential when dealing with occlusions. In the experimental part of this paper we present classification results for five classes of simple geometrical figures: prism, cylinder, half cylinder, a cube, and a bridge. We compare classification results for three classical feature extractors: Fourier descriptors, pseudo Zernike and Zernike moments.

  18. Neural substrates of view-invariant object recognition developed without experiencing rotations of the objects.

    PubMed

    Okamura, Jun-Ya; Yamaguchi, Reona; Honda, Kazunari; Wang, Gang; Tanaka, Keiji

    2014-11-01

    One fails to recognize an unfamiliar object across changes in viewing angle when it must be discriminated from similar distractor objects. View-invariant recognition gradually develops as the viewer repeatedly sees the objects in rotation. It is assumed that different views of each object are associated with one another while their successive appearance is experienced in rotation. However, natural experience of objects also contains ample opportunities to discriminate among objects at each of the multiple viewing angles. Our previous behavioral experiments showed that after experiencing a new set of object stimuli during a task that required only discrimination at each of four viewing angles at 30° intervals, monkeys could recognize the objects across changes in viewing angle up to 60°. By recording activities of neurons from the inferotemporal cortex after various types of preparatory experience, we here found a possible neural substrate for the monkeys' performance. For object sets that the monkeys had experienced during the task that required only discrimination at each of four viewing angles, many inferotemporal neurons showed object selectivity covering multiple views. The degree of view generalization found for these object sets was similar to that found for stimulus sets with which the monkeys had been trained to conduct view-invariant recognition. These results suggest that the experience of discriminating new objects in each of several viewing angles develops the partially view-generalized object selectivity distributed over many neurons in the inferotemporal cortex, which in turn bases the monkeys' emergent capability to discriminate the objects across changes in viewing angle.

  19. Vision: are models of object recognition catching up with the brain?

    PubMed

    Poggio, Tomaso; Ullman, Shimon

    2013-12-01

    Object recognition has been a central yet elusive goal of computational vision. For many years, computer performance seemed highly deficient and unable to emulate the basic capabilities of the human recognition system. Over the past decade or so, computer scientists and neuroscientists have developed algorithms and systems-and models of visual cortex-that have come much closer to human performance in visual identification and categorization. In this personal perspective, we discuss the ongoing struggle of visual models to catch up with the visual cortex, identify key reasons for the relatively rapid improvement of artificial systems and models, and identify open problems for computational vision in this domain.

  20. Fractional Fourier transform pre-processing for neural networks and its application to object recognition.

    PubMed

    Barshan, Billur; Ayrulu, Birsel

    2002-01-01

    This study investigates fractional Fourier transform pre-processing of input signals to neural networks. The fractional Fourier transform is a generalization of the ordinary Fourier transform with an order parameter a. Judicious choice of this parameter can lead to overall improvement of the neural network performance. As an illustrative example, we consider recognition and position estimation of different types of objects based on their sonar returns. Raw amplitude and time-of-flight patterns acquired from a real sonar system are processed, demonstrating reduced error in both recognition and position estimation of objects.

  1. Unsupervised learning of probabilistic object models (POMs) for object classification, segmentation, and recognition using knowledge propagation.

    PubMed

    Chen, Yuanhao; Zhu, Long Leo; Yuille, Alan; Zhang, Hongjiang

    2009-10-01

    We present a method to learn probabilistic object models (POMs) with minimal supervision, which exploit different visual cues and perform tasks such as classification, segmentation, and recognition. We formulate this as a structure induction and learning task and our strategy is to learn and combine elementary POMs that make use of complementary image cues. We describe a novel structure induction procedure, which uses knowledge propagation to enable POMs to provide information to other POMs and "teach them" (which greatly reduces the amount of supervision required for training and speeds up the inference). In particular, we learn a POM-IP defined on Interest Points using weak supervision [1], [2] and use this to train a POM-mask, defined on regional features, which yields a combined POM that performs segmentation/localization. This combined model can be used to train POM-edgelets, defined on edgelets, which gives a full POM with improved performance on classification. We give detailed experimental analysis on large data sets for classification and segmentation with comparison to other methods. Inference takes five seconds while learning takes approximately four hours. In addition, we show that the full POM is invariant to scale and rotation of the object (for learning and inference) and can learn hybrid objects classes (i.e., when there are several objects and the identity of the object in each image is unknown). Finally, we show that POMs can be used to match between different objects of the same category, and hence, enable objects recognition. PMID:19696447

  2. Primitive Solar System Objects

    NASA Astrophysics Data System (ADS)

    Jewitt, David

    1999-10-01

    Some of the most fundamental and topical questions in astronomy concern the origin and evolution of planetary systems. In the solar system, these questions are most directly addressed through observations of chemically and physically primitive bodies in which a record of the initial conditions may be preserved. The most primitive materials in the solar system reside near its outer edge, in a trans-Neptunian ring known as the Kuiper Belt and in a surrounding spherical cloud first postulated by Oort. These regions supply comets to the inner solar system and, in the case of the Kuiper Belt, preserve evidence of dynamical processes operative in the first 100 million years after formation. The Kuiper Belt is also a source of collisionally produced dust and may be analogous to the dusty rings observed encircling a number of nearby main-sequence stars. I will review the currently known properties of these primitive objects, and discuss how ALMA can contribute to our understanding of the early solar system.

  3. Category Specificity in Normal Episodic Learning: Applications to Object Recognition and Category-Specific Agnosia

    ERIC Educational Resources Information Center

    Bukach, Cindy M.; Bub, Daniel N.; Masson, Michael E. J.; Lindsay, D. Stephen

    2004-01-01

    Studies of patients with category-specific agnosia (CSA) have given rise to multiple theories of object recognition, most of which assume the existence of a stable, abstract semantic memory system. We applied an episodic view of memory to questions raised by CSA in a series of studies examining normal observers' recall of newly learned attributes…

  4. Combining depth and color data for 3D object recognition

    NASA Astrophysics Data System (ADS)

    Joergensen, Thomas M.; Linneberg, Christian; Andersen, Allan W.

    1997-09-01

    This paper describes the shape recognition system that has been developed within the ESPRIT project 9052 ADAS on automatic disassembly of TV-sets using a robot cell. Depth data from a chirped laser radar are fused with color data from a video camera. The sensor data is pre-processed in several ways and the obtained representation is used to train a RAM neural network (memory based reasoning approach) to detect different components within TV-sets. The shape recognizing architecture has been implemented and tested in a demonstration setup.

  5. Plastic modifications induced by object recognition memory processing

    PubMed Central

    Clarke, Julia Rosauro; Cammarota, Martín; Gruart, Agnès; Izquierdo, Iván; Delgado-García, José María

    2010-01-01

    Long-term potentiation (LTP) phenomenon is widely accepted as a cellular model of memory consolidation. Object recognition (OR) is a particularly useful way of studying declarative memory in rodents because it makes use of their innate preference for novel over familiar objects. In this study, mice had electrodes implanted in the hippocampal Schaffer collaterals–pyramidal CA1 pathway and were trained for OR. Field EPSPs evoked at the CA3-CA1 synapse were recorded at the moment of training and at different times thereafter. LTP-like synaptic enhancement was found 6 h posttraining. A testing session was conducted 24 h after training, in the presence of one familiar and one novel object. Hippocampal synaptic facilitation was observed during exploration of familiar and novel objects. A short depotentiation period was observed early after the test and was followed by a later phase of synaptic efficacy enhancement. Here, we show that OR memory consolidation is accompanied by transient potentiation in the hippocampal CA3-CA1 synapses, while reconsolidation of this memory requires a short-lasting phase of depotentiation that could account for its well described vulnerability. The late synaptic enhancement phase, on the other hand, would be a consequence of memory restabilization. PMID:20133798

  6. Plastic modifications induced by object recognition memory processing.

    PubMed

    Clarke, Julia Rosauro; Cammarota, Martín; Gruart, Agnès; Izquierdo, Iván; Delgado-García, José María

    2010-02-01

    Long-term potentiation (LTP) phenomenon is widely accepted as a cellular model of memory consolidation. Object recognition (OR) is a particularly useful way of studying declarative memory in rodents because it makes use of their innate preference for novel over familiar objects. In this study, mice had electrodes implanted in the hippocampal Schaffer collaterals-pyramidal CA1 pathway and were trained for OR. Field EPSPs evoked at the CA3-CA1 synapse were recorded at the moment of training and at different times thereafter. LTP-like synaptic enhancement was found 6 h posttraining. A testing session was conducted 24 h after training, in the presence of one familiar and one novel object. Hippocampal synaptic facilitation was observed during exploration of familiar and novel objects. A short depotentiation period was observed early after the test and was followed by a later phase of synaptic efficacy enhancement. Here, we show that OR memory consolidation is accompanied by transient potentiation in the hippocampal CA3-CA1 synapses, while reconsolidation of this memory requires a short-lasting phase of depotentiation that could account for its well described vulnerability. The late synaptic enhancement phase, on the other hand, would be a consequence of memory restabilization.

  7. Exploring local regularities for 3D object recognition

    NASA Astrophysics Data System (ADS)

    Tian, Huaiwen; Qin, Shengfeng

    2016-09-01

    In order to find better simplicity measurements for 3D object recognition, a new set of local regularities is developed and tested in a stepwise 3D reconstruction method, including localized minimizing standard deviation of angles(L-MSDA), localized minimizing standard deviation of segment magnitudes(L-MSDSM), localized minimum standard deviation of areas of child faces (L-MSDAF), localized minimum sum of segment magnitudes of common edges (L-MSSM), and localized minimum sum of areas of child face (L-MSAF). Based on their effectiveness measurements in terms of form and size distortions, it is found that when two local regularities: L-MSDA and L-MSDSM are combined together, they can produce better performance. In addition, the best weightings for them to work together are identified as 10% for L-MSDSM and 90% for L-MSDA. The test results show that the combined usage of L-MSDA and L-MSDSM with identified weightings has a potential to be applied in other optimization based 3D recognition methods to improve their efficacy and robustness.

  8. Joint Tensor Feature Analysis For Visual Object Recognition.

    PubMed

    Wong, Wai Keung; Lai, Zhihui; Xu, Yong; Wen, Jiajun; Ho, Chu Po

    2015-11-01

    Tensor-based object recognition has been widely studied in the past several years. This paper focuses on the issue of joint feature selection from the tensor data and proposes a novel method called joint tensor feature analysis (JTFA) for tensor feature extraction and recognition. In order to obtain a set of jointly sparse projections for tensor feature extraction, we define the modified within-class tensor scatter value and the modified between-class tensor scatter value for regression. The k-mode optimization technique and the L(2,1)-norm jointly sparse regression are combined together to compute the optimal solutions. The convergent analysis, computational complexity analysis and the essence of the proposed method/model are also presented. It is interesting to show that the proposed method is very similar to singular value decomposition on the scatter matrix but with sparsity constraint on the right singular value matrix or eigen-decomposition on the scatter matrix with sparse manner. Experimental results on some tensor datasets indicate that JTFA outperforms some well-known tensor feature extraction and selection algorithms. PMID:26470058

  9. Combining feature- and correspondence-based methods for visual object recognition.

    PubMed

    Westphal, Günter; Würtz, Rolf P

    2009-07-01

    We present an object recognition system built on a combination of feature- and correspondence-based pattern recognizers. The feature-based part, called preselection network, is a single-layer feedforward network weighted with the amount of information contributed by each feature to the decision at hand. For processing arbitrary objects, we employ small, regular graphs whose nodes are attributed with Gabor amplitudes, termed parquet graphs. The preselection network can quickly rule out most irrelevant matches and leaves only the ambiguous cases, so-called model candidates, to be verified by a rudimentary version of elastic graph matching, a standard correspondence-based technique for face and object recognition. According to the model, graphs are constructed that describe the object in the input image well. We report the results of experiments on standard databases for object recognition. The method achieved high recognition rates on identity and pose. Unlike many other models, it can also cope with varying background, multiple objects, and partial occlusion.

  10. Cross-modal conflicts in object recognition: determining the influence of object category.

    PubMed

    Vogler, Jessica N; Titchener, Kirsteen

    2011-10-01

    Previous research examining cross-modal conflicts in object recognition has often made use of animal vocalizations and images, which may be considered natural and ecologically valid, thus strengthening the association in the congruent condition. The current research tested whether the same cross-modal conflict would exist for man-made object sounds as well as comparing the speed and accuracy of auditory processing across the two object categories. Participants were required to attend to a sound paired with a visual stimulus and then respond to a verification item (e.g., "Dog?"). Sounds were congruent (same object), neutral (unidentifiable image), or incongruent (different object) with the images presented. In the congruent and neutral condition, animals were recognized significantly faster and with greater accuracy than man-made objects. It was hypothesized that in the incongruent condition, no difference in reaction time or error rate would be found between animals and man-made objects. This prediction was not supported, indicating that the association between an object's sound and image may not be that disparate when comparing animals to man-made objects. The findings further support cross-modal conflict research for both the animal and man-made object category. The most important finding, however, was that auditory processing is enhanced for living compared to nonliving objects, a difference only previously found in visual processing. Implications relevant to both the neuropsychological literature and sound research are discussed. PMID:21912929

  11. Cross-modal conflicts in object recognition: determining the influence of object category.

    PubMed

    Vogler, Jessica N; Titchener, Kirsteen

    2011-10-01

    Previous research examining cross-modal conflicts in object recognition has often made use of animal vocalizations and images, which may be considered natural and ecologically valid, thus strengthening the association in the congruent condition. The current research tested whether the same cross-modal conflict would exist for man-made object sounds as well as comparing the speed and accuracy of auditory processing across the two object categories. Participants were required to attend to a sound paired with a visual stimulus and then respond to a verification item (e.g., "Dog?"). Sounds were congruent (same object), neutral (unidentifiable image), or incongruent (different object) with the images presented. In the congruent and neutral condition, animals were recognized significantly faster and with greater accuracy than man-made objects. It was hypothesized that in the incongruent condition, no difference in reaction time or error rate would be found between animals and man-made objects. This prediction was not supported, indicating that the association between an object's sound and image may not be that disparate when comparing animals to man-made objects. The findings further support cross-modal conflict research for both the animal and man-made object category. The most important finding, however, was that auditory processing is enhanced for living compared to nonliving objects, a difference only previously found in visual processing. Implications relevant to both the neuropsychological literature and sound research are discussed.

  12. Three-dimensional object recognition by monocular and multisensory perception: Space robotics application

    NASA Astrophysics Data System (ADS)

    Pampagnin, Luc-Henry

    Three dimensional object recognition by computer vision is studied with particular consideration to modelization and graph utilization. First a polyhedral object is identified and located from one intensity image; the model is known and the object is convex or not. The model consists of a geometrical model and the perspective projection aspect graph. Recognition was based on construction and utilization of the compatibility graph, expressing geometrical and visual constraints between matchings of high level entities (object faces). A multisensory system is described, whose main advantage is the complementarity of different kinds of information, which eliminates the limitations due to the use of one image. The system included a black and white camera and a pan and tilt scanning laser range finder.

  13. Laptop Computer - Based Facial Recognition System Assessment

    SciTech Connect

    R. A. Cain; G. B. Singleton

    2001-03-01

    The objective of this project was to assess the performance of the leading commercial-off-the-shelf (COTS) facial recognition software package when used as a laptop application. We performed the assessment to determine the system's usefulness for enrolling facial images in a database from remote locations and conducting real-time searches against a database of previously enrolled images. The assessment involved creating a database of 40 images and conducting 2 series of tests to determine the product's ability to recognize and match subject faces under varying conditions. This report describes the test results and includes a description of the factors affecting the results. After an extensive market survey, we selected Visionics' FaceIt{reg_sign} software package for evaluation and a review of the Facial Recognition Vendor Test 2000 (FRVT 2000). This test was co-sponsored by the US Department of Defense (DOD) Counterdrug Technology Development Program Office, the National Institute of Justice, and the Defense Advanced Research Projects Agency (DARPA). Administered in May-June 2000, the FRVT 2000 assessed the capabilities of facial recognition systems that were currently available for purchase on the US market. Our selection of this Visionics product does not indicate that it is the ''best'' facial recognition software package for all uses. It was the most appropriate package based on the specific applications and requirements for this specific application. In this assessment, the system configuration was evaluated for effectiveness in identifying individuals by searching for facial images captured from video displays against those stored in a facial image database. An additional criterion was that the system be capable of operating discretely. For this application, an operational facial recognition system would consist of one central computer hosting the master image database with multiple standalone systems configured with duplicates of the master operating in

  14. Anthropomorphic robot for recognition and drawing generalized object images

    NASA Astrophysics Data System (ADS)

    Ginzburg, Vera M.

    1998-10-01

    The process of recognition, for instance, understanding the text, written by different fonts, consists in the depriving of the individual attributes of the letters in the particular font. It is shown that such process, in Nature and technique, can be provided by the narrowing the spatial frequency of the object's image by its defocusing. In defocusing images remain only areas, so-called Informative Fragments (IFs), which all together form the generalized (stylized) image of many identical objects. It is shown that the variety of shapes of IFs is restricted and can be presented by `Geometrical alphabet'. The `letters' for this alphabet can be created using two basic `genetic' figures: a stripe and round spot. It is known from physiology that the special cells of visual cortex response to these particular figures. The prototype of such `genetic' alphabet has been made using Boolean algebra (Venn's diagrams). The algorithm for drawing the letter's (`genlet's') shape in this alphabet and generalized images of objects (for example, `sleeping cat'), are given. A scheme of an anthropomorphic robot is shown together with results of model computer experiment of the robot's action--`drawing' the generalized image.

  15. A real-time optical automatic target recognition system

    NASA Astrophysics Data System (ADS)

    Chen, Huaixin; Nan, Jianshe; Li, Xiaosun; Wei, Honggang

    2004-04-01

    Automatic target recognition (ATR) technique has been applied in both civil and military. In this paper, we present a new optical pattern recognition system for target recognition. This system includes synthetic discriminate function (SDF) based practical optimized filters for the 3-D targets, the Reference Filter Libs for high correlation SNR, the mapping between the input (object regions) and the output (correlation peaks), and neural networks (ANN) for final decision making. The Real-time optical target recognition is realized by temporal multiplexing technique with electronic addressing spatial light modulator. The experiment results show that the proposed OPR system is efficient and reliable.

  16. Fast and robust recognition and localization of 2D objects

    NASA Astrophysics Data System (ADS)

    Otterbach, Rainer; Gerdes, Rolf; Kammueller, R.

    1994-11-01

    The paper presents a vision system which provides a robust model-based identification and localization of 2-D objects in industrial scenes. A symbolic image description based on the polygonal approximation of the object silhouettes is extracted in video real time by the use of dedicated hardware. Candidate objects are selected from the model database using a time and memory efficient hashing algorithm. Any candidate object is submitted to the next computation stage which generates pose hypotheses by assigning model to image contours. Corresponding continuous measures of similarity are derived from the turning functions of the curves. Finally, the previous generated hypotheses are verified using a voting scheme in transformation space. Experimental results reveal the fault tolerance of the vision system with regard to noisy and split image contours as well as partial occlusion of objects. THe short cycle time and the easy adaptability of the vision system make it well suited for a wide variety of applications in industrial automation.

  17. Visual object recognition for automatic micropropagation of plants

    NASA Astrophysics Data System (ADS)

    Brendel, Thorsten; Schwanke, Joerg; Jensch, Peter F.

    1994-11-01

    Micropropagation of plants is done by cutting juvenile plants and placing them into special container-boxes with nutrient-solution where the pieces can grow up and be cut again several times. To produce high amounts of biomass it is necessary to do plant micropropagation by a robotic system. In this paper we describe parts of the vision system that recognizes plants and their particular cutting points. Therefore, it is necessary to extract elements of the plants and relations between these elements (for example root, stem, leaf). Different species vary in their morphological appearance, variation is also immanent in plants of the same species. Therefore, we introduce several morphological classes of plants from that we expect same recognition methods.

  18. 3-D Object Recognition from Point Cloud Data

    NASA Astrophysics Data System (ADS)

    Smith, W.; Walker, A. S.; Zhang, B.

    2011-09-01

    The market for real-time 3-D mapping includes not only traditional geospatial applications but also navigation of unmanned autonomous vehicles (UAVs). Massively parallel processes such as graphics processing unit (GPU) computing make real-time 3-D object recognition and mapping achievable. Geospatial technologies such as digital photogrammetry and GIS offer advanced capabilities to produce 2-D and 3-D static maps using UAV data. The goal is to develop real-time UAV navigation through increased automation. It is challenging for a computer to identify a 3-D object such as a car, a tree or a house, yet automatic 3-D object recognition is essential to increasing the productivity of geospatial data such as 3-D city site models. In the past three decades, researchers have used radiometric properties to identify objects in digital imagery with limited success, because these properties vary considerably from image to image. Consequently, our team has developed software that recognizes certain types of 3-D objects within 3-D point clouds. Although our software is developed for modeling, simulation and visualization, it has the potential to be valuable in robotics and UAV applications. The locations and shapes of 3-D objects such as buildings and trees are easily recognizable by a human from a brief glance at a representation of a point cloud such as terrain-shaded relief. The algorithms to extract these objects have been developed and require only the point cloud and minimal human inputs such as a set of limits on building size and a request to turn on a squaring option. The algorithms use both digital surface model (DSM) and digital elevation model (DEM), so software has also been developed to derive the latter from the former. The process continues through the following steps: identify and group 3-D object points into regions; separate buildings and houses from trees; trace region boundaries; regularize and simplify boundary polygons; construct complex roofs. Several case

  19. Emerging technologies with potential for objectively evaluating speech recognition skills.

    PubMed

    Rawool, Vishakha Waman

    2016-01-01

    Work-related exposure to noise and other ototoxins can cause damage to the cochlea, synapses between the inner hair cells, the auditory nerve fibers, and higher auditory pathways, leading to difficulties in recognizing speech. Procedures designed to determine speech recognition scores (SRS) in an objective manner can be helpful in disability compensation cases where the worker claims to have poor speech perception due to exposure to noise or ototoxins. Such measures can also be helpful in determining SRS in individuals who cannot provide reliable responses to speech stimuli, including patients with Alzheimer's disease, traumatic brain injuries, and infants with and without hearing loss. Cost-effective neural monitoring hardware and software is being rapidly refined due to the high demand for neurogaming (games involving the use of brain-computer interfaces), health, and other applications. More specifically, two related advances in neuro-technology include relative ease in recording neural activity and availability of sophisticated analysing techniques. These techniques are reviewed in the current article and their applications for developing objective SRS procedures are proposed. Issues related to neuroaudioethics (ethics related to collection of neural data evoked by auditory stimuli including speech) and neurosecurity (preservation of a person's neural mechanisms and free will) are also discussed.

  20. Image quality analysis and improvement of Ladar reflective tomography for space object recognition

    NASA Astrophysics Data System (ADS)

    Wang, Jin-cheng; Zhou, Shi-wei; Shi, Liang; Hu, Yi-Hua; Wang, Yong

    2016-01-01

    Some problems in the application of Ladar reflective tomography for space object recognition are studied in this work. An analytic target model is adopted to investigate the image reconstruction properties with limited relative angle range, which are useful to verify the target shape from the incomplete image, analyze the shadowing effect of the target and design the satellite payloads against recognition via reflective tomography approach. We proposed an iterative maximum likelihood method basing on Bayesian theory, which can effectively compress the pulse width and greatly improve the image resolution of incoherent LRT system without loss of signal to noise ratio.

  1. Recognition of 3-D Scene with Partially Occluded Objects

    NASA Astrophysics Data System (ADS)

    Lu, Siwei; Wong, Andrew K. C...

    1987-03-01

    This paper presents a robot vision system which is capable of recognizing objects in a 3-D scene and interpreting their spatial relation even though some objects in the scene may be partially occluded by other objects. An algorithm is developed to transform the geometric information from the range data into an attributed hypergraph representation (AHR). A hypergraph monomorphism algorithm is then used to compare the AHR of objects in the scene with a set of complete AHR's of prototypes. The capability of identifying connected components and interpreting various types of edges in the 3-D scene enables us to distinguish objects which are partially blocking each other in the scene. Using structural information stored in the primitive area graph, a heuristic hypergraph monomorphism algorithm provides an effective way for recognizing, locating, and interpreting partially occluded objects in the range image.

  2. Moving Object Control System

    NASA Technical Reports Server (NTRS)

    Arndt, G. Dickey (Inventor); Carl, James R. (Inventor)

    2001-01-01

    A method is provided for controlling two objects relatively moveable with respect to each other. A plurality of receivers are provided for detecting a distinctive microwave signal from each of the objects and measuring the phase thereof with respect to a reference signal. The measured phase signal is used to determine a distance between each of the objects and each of the plurality of receivers. Control signals produced in response to the relative distances are used to control the position of the two objects.

  3. Learning invariant object recognition from temporal correlation in a hierarchical network.

    PubMed

    Lessmann, Markus; Würtz, Rolf P

    2014-06-01

    Invariant object recognition, which means the recognition of object categories independent of conditions like viewing angle, scale and illumination, is a task of great interest that humans can fulfill much better than artificial systems. During the last years several basic principles were derived from neurophysiological observations and careful consideration: (1) Developing invariance to possible transformations of the object by learning temporal sequences of visual features that occur during the respective alterations. (2) Learning in a hierarchical structure, so basic level (visual) knowledge can be reused for different kinds of objects. (3) Using feedback to compare predicted input with the current one for choosing an interpretation in the case of ambiguous signals. In this paper we propose a network which implements all of these concepts in a computationally efficient manner which gives very good results on standard object datasets. By dynamically switching off weakly active neurons and pruning weights computation is sped up and thus handling of large databases with several thousands of images and a number of categories in a similar order becomes possible. The involved parameters allow flexible adaptation to the information content of training data and allow tuning to different databases relatively easily. Precondition for successful learning is that training images are presented in an order assuring that images of the same object under similar viewing conditions follow each other. Through an implementation with sparse data structures the system has moderate memory demands and still yields very good recognition rates. PMID:24657573

  4. Real-time concealed-object detection and recognition with passive millimeter wave imaging.

    PubMed

    Yeom, Seokwon; Lee, Dong-Su; Jang, Yushin; Lee, Mun-Kyo; Jung, Sang-Won

    2012-04-23

    Millimeter wave (MMW) imaging is finding rapid adoption in security applications such as concealed object detection under clothing. A passive MMW imaging system can operate as a stand-off type sensor that scans people in both indoors and outdoors. However, the imaging system often suffers from the diffraction limit and the low signal level. Therefore, suitable intelligent image processing algorithms would be required for automatic detection and recognition of the concealed objects. This paper proposes real-time outdoor concealed-object detection and recognition with a radiometric imaging system. The concealed object region is extracted by the multi-level segmentation. A novel approach is proposed to measure similarity between two binary images. Principal component analysis (PCA) regularizes the shape in terms of translation and rotation. A geometric-based feature vector is composed of shape descriptors, which can achieve scale and orientation-invariant and distortion-tolerant property. Class is decided by minimum Euclidean distance between normalized feature vectors. Experiments confirm that the proposed methods provide fast and reliable recognition of the concealed object carried by a moving human subject.

  5. Cross-modal object recognition and dynamic weighting of sensory inputs in a fish.

    PubMed

    Schumacher, Sarah; Burt de Perera, Theresa; Thenert, Johanna; von der Emde, Gerhard

    2016-07-01

    Most animals use multiple sensory modalities to obtain information about objects in their environment. There is a clear adaptive advantage to being able to recognize objects cross-modally and spontaneously (without prior training with the sense being tested) as this increases the flexibility of a multisensory system, allowing an animal to perceive its world more accurately and react to environmental changes more rapidly. So far, spontaneous cross-modal object recognition has only been shown in a few mammalian species, raising the question as to whether such a high-level function may be associated with complex mammalian brain structures, and therefore absent in animals lacking a cerebral cortex. Here we use an object-discrimination paradigm based on operant conditioning to show, for the first time to our knowledge, that a nonmammalian vertebrate, the weakly electric fish Gnathonemus petersii, is capable of performing spontaneous cross-modal object recognition and that the sensory inputs are weighted dynamically during this task. We found that fish trained to discriminate between two objects with either vision or the active electric sense, were subsequently able to accomplish the task using only the untrained sense. Furthermore we show that cross-modal object recognition is influenced by a dynamic weighting of the sensory inputs. The fish weight object-related sensory inputs according to their reliability, to minimize uncertainty and to enable an optimal integration of the senses. Our results show that spontaneous cross-modal object recognition and dynamic weighting of sensory inputs are present in a nonmammalian vertebrate. PMID:27313211

  6. Crowding, grouping, and object recognition: A matter of appearance

    PubMed Central

    Herzog, Michael H.; Sayim, Bilge; Chicherov, Vitaly; Manassi, Mauro

    2015-01-01

    In crowding, the perception of a target strongly deteriorates when neighboring elements are presented. Crowding is usually assumed to have the following characteristics. (a) Crowding is determined only by nearby elements within a restricted region around the target (Bouma's law). (b) Increasing the number of flankers can only deteriorate performance. (c) Target-flanker interference is feature-specific. These characteristics are usually explained by pooling models, which are well in the spirit of classic models of object recognition. In this review, we summarize recent findings showing that crowding is not determined by the above characteristics, thus, challenging most models of crowding. We propose that the spatial configuration across the entire visual field determines crowding. Only when one understands how all elements of a visual scene group with each other, can one determine crowding strength. We put forward the hypothesis that appearance (i.e., how stimuli look) is a good predictor for crowding, because both crowding and appearance reflect the output of recurrent processing rather than interactions during the initial phase of visual processing. PMID:26024452

  7. Crowding, grouping, and object recognition: A matter of appearance.

    PubMed

    Herzog, Michael H; Sayim, Bilge; Chicherov, Vitaly; Manassi, Mauro

    2015-01-01

    In crowding, the perception of a target strongly deteriorates when neighboring elements are presented. Crowding is usually assumed to have the following characteristics. (a) Crowding is determined only by nearby elements within a restricted region around the target (Bouma's law). (b) Increasing the number of flankers can only deteriorate performance. (c) Target-flanker interference is feature-specific. These characteristics are usually explained by pooling models, which are well in the spirit of classic models of object recognition. In this review, we summarize recent findings showing that crowding is not determined by the above characteristics, thus, challenging most models of crowding. We propose that the spatial configuration across the entire visual field determines crowding. Only when one understands how all elements of a visual scene group with each other, can one determine crowding strength. We put forward the hypothesis that appearance (i.e., how stimuli look) is a good predictor for crowding, because both crowding and appearance reflect the output of recurrent processing rather than interactions during the initial phase of visual processing.

  8. Communicative Signals Promote Object Recognition Memory and Modulate the Right Posterior STS.

    PubMed

    Redcay, Elizabeth; Ludlum, Ruth S; Velnoskey, Kayla R; Kanwal, Simren

    2016-01-01

    Detection of communicative signals is thought to facilitate knowledge acquisition early in life, but less is known about the role these signals play in adult learning or about the brain systems supporting sensitivity to communicative intent. The current study examined how ostensive gaze cues and communicative actions affect adult recognition memory and modulate neural activity as measured by fMRI. For both the behavioral and fMRI experiments, participants viewed a series of videos of an actress acting on one of two objects in front of her. Communicative context in the videos was manipulated in a 2 × 2 design in which the actress either had direct gaze (Gaze) or wore a visor (NoGaze) and either pointed at (Point) or reached for (Reach) one of the objects (target) in front of her. Participants then completed a recognition memory task with old (target and nontarget) objects and novel objects. Recognition memory for target objects in the Gaze conditions was greater than NoGaze, but no effects of gesture type were seen. Similarly, the fMRI video-viewing task revealed a significant effect of Gaze within right posterior STS (pSTS), but no significant effects of Gesture. Furthermore, pSTS sensitivity to Gaze conditions was related to greater memory for objects viewed in Gaze, as compared with NoGaze, conditions. Taken together, these results demonstrate that the ostensive, communicative signal of direct gaze preceding an object-directed action enhances recognition memory for attended items and modulates the pSTS response to object-directed actions. Thus, establishment of a communicative context through ostensive signals remains an important component of learning and memory into adulthood, and the pSTS may play a role in facilitating this type of social learning.

  9. 3D video analysis of the novel object recognition test in rats.

    PubMed

    Matsumoto, Jumpei; Uehara, Takashi; Urakawa, Susumu; Takamura, Yusaku; Sumiyoshi, Tomiki; Suzuki, Michio; Ono, Taketoshi; Nishijo, Hisao

    2014-10-01

    The novel object recognition (NOR) test has been widely used to test memory function. We developed a 3D computerized video analysis system that estimates nose contact with an object in Long Evans rats to analyze object exploration during NOR tests. The results indicate that the 3D system reproducibly and accurately scores the NOR test. Furthermore, the 3D system captures a 3D trajectory of the nose during object exploration, enabling detailed analyses of spatiotemporal patterns of object exploration. The 3D trajectory analysis revealed a specific pattern of object exploration in the sample phase of the NOR test: normal rats first explored the lower parts of objects and then gradually explored the upper parts. A systematic injection of MK-801 suppressed changes in these exploration patterns. The results, along with those of previous studies, suggest that the changes in the exploration patterns reflect neophobia to a novel object and/or changes from spatial learning to object learning. These results demonstrate that the 3D tracking system is useful not only for detailed scoring of animal behaviors but also for investigation of characteristic spatiotemporal patterns of object exploration. The system has the potential to facilitate future investigation of neural mechanisms underlying object exploration that result from dynamic and complex brain activity. PMID:24991752

  10. Regulation of object recognition and object placement by ovarian sex steroid hormones

    PubMed Central

    Tuscher, Jennifer J.; Fortress, Ashley M.; Kim, Jaekyoon; Frick, Karyn M.

    2014-01-01

    The ovarian hormones 17β-estradiol (E2) and progesterone (P4) are potent modulators of hippocampal memory formation. Both hormones have been demonstrated to enhance hippocampal memory by regulating the cellular and molecular mechanisms thought to underlie memory formation. Behavioral neuroendocrinologists have increasingly used the object recognition and object placement (object location) tasks to investigate the role of E2 and P4 in regulating hippocampal memory formation in rodents. These one-trial learning tasks are ideal for studying acute effects of hormone treatments on different phases of memory because they can be administered during acquisition (pre-training), consolidation (post-training), or retrieval (pre-testing). This review synthesizes the rodent literature testing the effects of E2 and P4 on object recognition (OR) and object placement (OP), and the molecular mechanisms in the hippocampus supporting memory formation in these tasks. Some general trends emerge from the data. Among gonadally intact females, object memory tends to be best when E2 and P4 levels are elevated during the estrous cycle, pregnancy, and in middle age. In ovariectomized females, E2 given before or immediately after testing generally enhances OR and OP in young and middle-aged rats and mice, although effects are mixed in aged rodents. Effects of E2 treatment on OR 7and OP memory consolidation can be mediated by both classical estrogen receptors (ERα and ERβ), and depend on glutamate receptors (NMDA, mGluR1) and activation of numerous cell signaling cascades (e.g., ERK, PI3K/Akt, mTOR) and epigenetic processes (e.g., histone H3 acetylation, DNA methylation). Acute P4 treatment given immediately after training also enhances OR and OP in young and middle-aged ovariectomized females by activating similar cell signaling pathways as E2 (e.g., ERK, mTOR). The few studies that have administered both hormones in combination suggest that treatment can enhance OR and OP, but that

  11. Histogram of Gabor phase patterns (HGPP): a novel object representation approach for face recognition.

    PubMed

    Zhang, Baochang; Shan, Shiguang; Chen, Xilin; Gao, Wen

    2007-01-01

    A novel object descriptor, histogram of Gabor phase pattern (HGPP), is proposed for robust face recognition. In HGPP, the quadrant-bit codes are first extracted from faces based on the Gabor transformation. Global Gabor phase pattern (GGPP) and local Gabor phase pattern (LGPP) are then proposed to encode the phase variations. GGPP captures the variations derived from the orientation changing of Gabor wavelet at a given scale (frequency), while LGPP encodes the local neighborhood variations by using a novel local XOR pattern (LXP) operator. They are both divided into the nonoverlapping rectangular regions, from which spatial histograms are extracted and concatenated into an extended histogram feature to represent the original image. Finally, the recognition is performed by using the nearest-neighbor classifier with histogram intersection as the similarity measurement. The features of HGPP lie in two aspects: 1) HGPP can describe the general face images robustly without the training procedure; 2) HGPP encodes the Gabor phase information, while most previous face recognition methods exploit the Gabor magnitude information. In addition, Fisher separation criterion is further used to improve the performance of HGPP by weighing the subregions of the image according to their discriminative powers. The proposed methods are successfully applied to face recognition, and the experiment results on the large-scale FERET and CAS-PEAL databases show that the proposed algorithms significantly outperform other well-known systems in terms of recognition rate.

  12. Privacy protection schemes for fingerprint recognition systems

    NASA Astrophysics Data System (ADS)

    Marasco, Emanuela; Cukic, Bojan

    2015-05-01

    The deployment of fingerprint recognition systems has always raised concerns related to personal privacy. A fingerprint is permanently associated with an individual and, generally, it cannot be reset if compromised in one application. Given that fingerprints are not a secret, potential misuses besides personal recognition represent privacy threats and may lead to public distrust. Privacy mechanisms control access to personal information and limit the likelihood of intrusions. In this paper, image- and feature-level schemes for privacy protection in fingerprint recognition systems are reviewed. Storing only key features of a biometric signature can reduce the likelihood of biometric data being used for unintended purposes. In biometric cryptosystems and biometric-based key release, the biometric component verifies the identity of the user, while the cryptographic key protects the communication channel. Transformation-based approaches only a transformed version of the original biometric signature is stored. Different applications can use different transforms. Matching is performed in the transformed domain which enable the preservation of low error rates. Since such templates do not reveal information about individuals, they are referred to as cancelable templates. A compromised template can be re-issued using a different transform. At image-level, de-identification schemes can remove identifiers disclosed for objectives unrelated to the original purpose, while permitting other authorized uses of personal information. Fingerprint images can be de-identified by, for example, mixing fingerprints or removing gender signature. In both cases, degradation of matching performance is minimized.

  13. It takes two-skilled recognition of objects engages lateral areas in both hemispheres.

    PubMed

    Bilalić, Merim; Kiesel, Andrea; Pohl, Carsten; Erb, Michael; Grodd, Wolfgang

    2011-01-01

    Our object recognition abilities, a direct product of our experience with objects, are fine-tuned to perfection. Left temporal and lateral areas along the dorsal, action related stream, as well as left infero-temporal areas along the ventral, object related stream are engaged in object recognition. Here we show that expertise modulates the activity of dorsal areas in the recognition of man-made objects with clearly specified functions. Expert chess players were faster than chess novices in identifying chess objects and their functional relations. Experts' advantage was domain-specific as there were no differences between groups in a control task featuring geometrical shapes. The pattern of eye movements supported the notion that experts' extensive knowledge about domain objects and their functions enabled superior recognition even when experts were not directly fixating the objects of interest. Functional magnetic resonance imaging (fMRI) related exclusively the areas along the dorsal stream to chess specific object recognition. Besides the commonly involved left temporal and parietal lateral brain areas, we found that only in experts homologous areas on the right hemisphere were also engaged in chess specific object recognition. Based on these results, we discuss whether skilled object recognition does not only involve a more efficient version of the processes found in non-skilled recognition, but also qualitatively different cognitive processes which engage additional brain areas. PMID:21283683

  14. 3D object recognition using kernel construction of phase wrapped images

    NASA Astrophysics Data System (ADS)

    Zhang, Hong; Su, Hongjun

    2011-06-01

    Kernel methods are effective machine learning techniques for many image based pattern recognition problems. Incorporating 3D information is useful in such applications. The optical profilometries and interforometric techniques provide 3D information in an implicit form. Typically phase unwrapping process, which is often hindered by the presence of noises, spots of low intensity modulation, and instability of the solutions, is applied to retrieve the proper depth information. In certain applications such as pattern recognition problems, the goal is to classify the 3D objects in the image, rather than to simply display or reconstruct them. In this paper we present a technique for constructing kernels on the measured data directly without explicit phase unwrapping. Such a kernel will naturally incorporate the 3D depth information and can be used to improve the systems involving 3D object analysis and classification.

  15. Research on recognition methods of aphid objects in complex backgrounds

    NASA Astrophysics Data System (ADS)

    Zhao, Hui-Yan; Zhang, Ji-Hong

    2009-07-01

    In order to improve the recognition accuracy among the kinds of aphids in the complex backgrounds, the recognition method among kinds of aphids based on Dual-Tree Complex Wavelet Transform (DT-CWT) and Support Vector Machine (Libsvm) is proposed. Firstly the image is pretreated; secondly the aphid images' texture feature of three crops are extracted by DT-CWT in order to get the training parameters of training model; finally the training model could recognize aphids among the three kinds of crops. By contrasting to Gabor wavelet transform and the traditional extracting texture's methods based on Gray-Level Co-Occurrence Matrix (GLCM), the experiment result shows that the method has a certain practicality and feasibility and provides basic for aphids' recognition between the identification among same kind aphid.

  16. Experience moderates overlap between object and face recognition, suggesting a common ability.

    PubMed

    Gauthier, Isabel; McGugin, Rankin W; Richler, Jennifer J; Herzmann, Grit; Speegle, Magen; Van Gulick, Ana E

    2014-07-03

    Some research finds that face recognition is largely independent from the recognition of other objects; a specialized and innate ability to recognize faces could therefore have little or nothing to do with our ability to recognize objects. We propose a new framework in which recognition performance for any category is the product of domain-general ability and category-specific experience. In Experiment 1, we show that the overlap between face and object recognition depends on experience with objects. In 256 subjects we measured face recognition, object recognition for eight categories, and self-reported experience with these categories. Experience predicted neither face recognition nor object recognition but moderated their relationship: Face recognition performance is increasingly similar to object recognition performance with increasing object experience. If a subject has a lot of experience with objects and is found to perform poorly, they also prove to have a low ability with faces. In a follow-up survey, we explored the dimensions of experience with objects that may have contributed to self-reported experience in Experiment 1. Different dimensions of experience appear to be more salient for different categories, with general self-reports of expertise reflecting judgments of verbal knowledge about a category more than judgments of visual performance. The complexity of experience and current limitations in its measurement support the importance of aggregating across multiple categories. Our findings imply that both face and object recognition are supported by a common, domain-general ability expressed through experience with a category and best measured when accounting for experience.

  17. Experience moderates overlap between object and face recognition, suggesting a common ability

    PubMed Central

    Gauthier, Isabel; McGugin, Rankin W.; Richler, Jennifer J.; Herzmann, Grit; Speegle, Magen; Van Gulick, Ana E.

    2014-01-01

    Some research finds that face recognition is largely independent from the recognition of other objects; a specialized and innate ability to recognize faces could therefore have little or nothing to do with our ability to recognize objects. We propose a new framework in which recognition performance for any category is the product of domain-general ability and category-specific experience. In Experiment 1, we show that the overlap between face and object recognition depends on experience with objects. In 256 subjects we measured face recognition, object recognition for eight categories, and self-reported experience with these categories. Experience predicted neither face recognition nor object recognition but moderated their relationship: Face recognition performance is increasingly similar to object recognition performance with increasing object experience. If a subject has a lot of experience with objects and is found to perform poorly, they also prove to have a low ability with faces. In a follow-up survey, we explored the dimensions of experience with objects that may have contributed to self-reported experience in Experiment 1. Different dimensions of experience appear to be more salient for different categories, with general self-reports of expertise reflecting judgments of verbal knowledge about a category more than judgments of visual performance. The complexity of experience and current limitations in its measurement support the importance of aggregating across multiple categories. Our findings imply that both face and object recognition are supported by a common, domain-general ability expressed through experience with a category and best measured when accounting for experience. PMID:24993021

  18. Insular Cortex Is Involved in Consolidation of Object Recognition Memory

    ERIC Educational Resources Information Center

    Bermudez-Rattoni, Federico; Okuda, Shoki; Roozendaal, Benno; McGaugh, James L.

    2005-01-01

    Extensive evidence indicates that the insular cortex (IC), also termed gustatory cortex, is critically involved in conditioned taste aversion and taste recognition memory. Although most studies of the involvement of the IC in memory have investigated taste, there is some evidence that the IC is involved in memory that is not based on taste. In…

  19. Higher-Order Neural Networks Applied to 2D and 3D Object Recognition

    NASA Technical Reports Server (NTRS)

    Spirkovska, Lilly; Reid, Max B.

    1994-01-01

    A Higher-Order Neural Network (HONN) can be designed to be invariant to geometric transformations such as scale, translation, and in-plane rotation. Invariances are built directly into the architecture of a HONN and do not need to be learned. Thus, for 2D object recognition, the network needs to be trained on just one view of each object class, not numerous scaled, translated, and rotated views. Because the 2D object recognition task is a component of the 3D object recognition task, built-in 2D invariance also decreases the size of the training set required for 3D object recognition. We present results for 2D object recognition both in simulation and within a robotic vision experiment and for 3D object recognition in simulation. We also compare our method to other approaches and show that HONNs have distinct advantages for position, scale, and rotation-invariant object recognition. The major drawback of HONNs is that the size of the input field is limited due to the memory required for the large number of interconnections in a fully connected network. We present partial connectivity strategies and a coarse-coding technique for overcoming this limitation and increasing the input field to that required by practical object recognition problems.

  20. Multifeatural shape processing in rats engaged in invariant visual object recognition.

    PubMed

    Alemi-Neissi, Alireza; Rosselli, Federica Bianca; Zoccolan, Davide

    2013-04-01

    The ability to recognize objects despite substantial variation in their appearance (e.g., because of position or size changes) represents such a formidable computational feat that it is widely assumed to be unique to primates. Such an assumption has restricted the investigation of its neuronal underpinnings to primate studies, which allow only a limited range of experimental approaches. In recent years, the increasingly powerful array of optical and molecular tools that has become available in rodents has spurred a renewed interest for rodent models of visual functions. However, evidence of primate-like visual object processing in rodents is still very limited and controversial. Here we show that rats are capable of an advanced recognition strategy, which relies on extracting the most informative object features across the variety of viewing conditions the animals may face. Rat visual strategy was uncovered by applying an image masking method that revealed the features used by the animals to discriminate two objects across a range of sizes, positions, in-depth, and in-plane rotations. Noticeably, rat recognition relied on a combination of multiple features that were mostly preserved across the transformations the objects underwent, and largely overlapped with the features that a simulated ideal observer deemed optimal to accomplish the discrimination task. These results indicate that rats are able to process and efficiently use shape information, in a way that is largely tolerant to variation in object appearance. This suggests that their visual system may serve as a powerful model to study the neuronal substrates of object recognition.

  1. Humans and Deep Networks Largely Agree on Which Kinds of Variation Make Object Recognition Harder.

    PubMed

    Kheradpisheh, Saeed R; Ghodrati, Masoud; Ganjtabesh, Mohammad; Masquelier, Timothée

    2016-01-01

    View-invariant object recognition is a challenging problem that has attracted much attention among the psychology, neuroscience, and computer vision communities. Humans are notoriously good at it, even if some variations are presumably more difficult to handle than others (e.g., 3D rotations). Humans are thought to solve the problem through hierarchical processing along the ventral stream, which progressively extracts more and more invariant visual features. This feed-forward architecture has inspired a new generation of bio-inspired computer vision systems called deep convolutional neural networks (DCNN), which are currently the best models for object recognition in natural images. Here, for the first time, we systematically compared human feed-forward vision and DCNNs at view-invariant object recognition task using the same set of images and controlling the kinds of transformation (position, scale, rotation in plane, and rotation in depth) as well as their magnitude, which we call "variation level." We used four object categories: car, ship, motorcycle, and animal. In total, 89 human subjects participated in 10 experiments in which they had to discriminate between two or four categories after rapid presentation with backward masking. We also tested two recent DCNNs (proposed respectively by Hinton's group and Zisserman's group) on the same tasks. We found that humans and DCNNs largely agreed on the relative difficulties of each kind of variation: rotation in depth is by far the hardest transformation to handle, followed by scale, then rotation in plane, and finally position (much easier). This suggests that DCNNs would be reasonable models of human feed-forward vision. In addition, our results show that the variation levels in rotation in depth and scale strongly modulate both humans' and DCNNs' recognition performances. We thus argue that these variations should be controlled in the image datasets used in vision research. PMID:27642281

  2. Humans and Deep Networks Largely Agree on Which Kinds of Variation Make Object Recognition Harder

    PubMed Central

    Kheradpisheh, Saeed R.; Ghodrati, Masoud; Ganjtabesh, Mohammad; Masquelier, Timothée

    2016-01-01

    View-invariant object recognition is a challenging problem that has attracted much attention among the psychology, neuroscience, and computer vision communities. Humans are notoriously good at it, even if some variations are presumably more difficult to handle than others (e.g., 3D rotations). Humans are thought to solve the problem through hierarchical processing along the ventral stream, which progressively extracts more and more invariant visual features. This feed-forward architecture has inspired a new generation of bio-inspired computer vision systems called deep convolutional neural networks (DCNN), which are currently the best models for object recognition in natural images. Here, for the first time, we systematically compared human feed-forward vision and DCNNs at view-invariant object recognition task using the same set of images and controlling the kinds of transformation (position, scale, rotation in plane, and rotation in depth) as well as their magnitude, which we call “variation level.” We used four object categories: car, ship, motorcycle, and animal. In total, 89 human subjects participated in 10 experiments in which they had to discriminate between two or four categories after rapid presentation with backward masking. We also tested two recent DCNNs (proposed respectively by Hinton's group and Zisserman's group) on the same tasks. We found that humans and DCNNs largely agreed on the relative difficulties of each kind of variation: rotation in depth is by far the hardest transformation to handle, followed by scale, then rotation in plane, and finally position (much easier). This suggests that DCNNs would be reasonable models of human feed-forward vision. In addition, our results show that the variation levels in rotation in depth and scale strongly modulate both humans' and DCNNs' recognition performances. We thus argue that these variations should be controlled in the image datasets used in vision research. PMID:27642281

  3. Humans and Deep Networks Largely Agree on Which Kinds of Variation Make Object Recognition Harder

    PubMed Central

    Kheradpisheh, Saeed R.; Ghodrati, Masoud; Ganjtabesh, Mohammad; Masquelier, Timothée

    2016-01-01

    View-invariant object recognition is a challenging problem that has attracted much attention among the psychology, neuroscience, and computer vision communities. Humans are notoriously good at it, even if some variations are presumably more difficult to handle than others (e.g., 3D rotations). Humans are thought to solve the problem through hierarchical processing along the ventral stream, which progressively extracts more and more invariant visual features. This feed-forward architecture has inspired a new generation of bio-inspired computer vision systems called deep convolutional neural networks (DCNN), which are currently the best models for object recognition in natural images. Here, for the first time, we systematically compared human feed-forward vision and DCNNs at view-invariant object recognition task using the same set of images and controlling the kinds of transformation (position, scale, rotation in plane, and rotation in depth) as well as their magnitude, which we call “variation level.” We used four object categories: car, ship, motorcycle, and animal. In total, 89 human subjects participated in 10 experiments in which they had to discriminate between two or four categories after rapid presentation with backward masking. We also tested two recent DCNNs (proposed respectively by Hinton's group and Zisserman's group) on the same tasks. We found that humans and DCNNs largely agreed on the relative difficulties of each kind of variation: rotation in depth is by far the hardest transformation to handle, followed by scale, then rotation in plane, and finally position (much easier). This suggests that DCNNs would be reasonable models of human feed-forward vision. In addition, our results show that the variation levels in rotation in depth and scale strongly modulate both humans' and DCNNs' recognition performances. We thus argue that these variations should be controlled in the image datasets used in vision research.

  4. Practical automatic Arabic license plate recognition system

    NASA Astrophysics Data System (ADS)

    Mohammad, Khader; Agaian, Sos; Saleh, Hani

    2011-02-01

    Since 1970's, the need of an automatic license plate recognition system, sometimes referred as Automatic License Plate Recognition system, has been increasing. A license plate recognition system is an automatic system that is able to recognize a license plate number, extracted from image sensors. In specific, Automatic License Plate Recognition systems are being used in conjunction with various transportation systems in application areas such as law enforcement (e.g. speed limit enforcement) and commercial usages such as parking enforcement and automatic toll payment private and public entrances, border control, theft and vandalism control. Vehicle license plate recognition has been intensively studied in many countries. Due to the different types of license plates being used, the requirement of an automatic license plate recognition system is different for each country. [License plate detection using cluster run length smoothing algorithm ].Generally, an automatic license plate localization and recognition system is made up of three modules; license plate localization, character segmentation and optical character recognition modules. This paper presents an Arabic license plate recognition system that is insensitive to character size, font, shape and orientation with extremely high accuracy rate. The proposed system is based on a combination of enhancement, license plate localization, morphological processing, and feature vector extraction using the Haar transform. The performance of the system is fast due to classification of alphabet and numerals based on the license plate organization. Experimental results for license plates of two different Arab countries show an average of 99 % successful license plate localization and recognition in a total of more than 20 different images captured from a complex outdoor environment. The results run times takes less time compared to conventional and many states of art methods.

  5. The Consolidation of Object and Context Recognition Memory Involve Different Regions of the Temporal Lobe

    ERIC Educational Resources Information Center

    Balderas, Israela; Rodriguez-Ortiz, Carlos J.; Salgado-Tonda, Paloma; Chavez-Hurtado, Julio; McGaugh, James L.; Bermudez-Rattoni, Federico

    2008-01-01

    These experiments investigated the involvement of several temporal lobe regions in consolidation of recognition memory. Anisomycin, a protein synthesis inhibitor, was infused into the hippocampus, perirhinal cortex, insular cortex, or basolateral amygdala of rats immediately after the sample phase of object or object-in-context recognition memory…

  6. Post-Training Reversible Inactivation of the Hippocampus Enhances Novel Object Recognition Memory

    ERIC Educational Resources Information Center

    Oliveira, Ana M. M.; Hawk, Joshua D.; Abel, Ted; Havekes, Robbert

    2010-01-01

    Research on the role of the hippocampus in object recognition memory has produced conflicting results. Previous studies have used permanent hippocampal lesions to assess the requirement for the hippocampus in the object recognition task. However, permanent hippocampal lesions may impact performance through effects on processes besides memory…

  7. Face Recognition Is Affected by Similarity in Spatial Frequency Range to a Greater Degree Than Within-Category Object Recognition

    ERIC Educational Resources Information Center

    Collin, Charles A.; Liu, Chang Hong; Troje, Nikolaus F.; McMullen, Patricia A.; Chaudhuri, Avi

    2004-01-01

    Previous studies have suggested that face identification is more sensitive to variations in spatial frequency content than object recognition, but none have compared how sensitive the 2 processes are to variations in spatial frequency overlap (SFO). The authors tested face and object matching accuracy under varying SFO conditions. Their results…

  8. Differential effects of spaced vs. massed training in long-term object-identity and object-location recognition memory.

    PubMed

    Bello-Medina, Paola C; Sánchez-Carrasco, Livia; González-Ornelas, Nadia R; Jeffery, Kathryn J; Ramírez-Amaya, Víctor

    2013-08-01

    Here we tested whether the well-known superiority of spaced training over massed training is equally evident in both object identity and object location recognition memory. We trained animals with objects placed in a variable or in a fixed location to produce a location-independent object identity memory or a location-dependent object representation. The training consisted of 5 trials that occurred either on one day (Massed) or over the course of 5 consecutive days (Spaced). The memory test was done in independent groups of animals either 24h or 7 days after the last training trial. In each test the animals were exposed to either a novel object, when trained with the objects in variable locations, or to a familiar object in a novel location, when trained with objects in fixed locations. The difference in time spent exploring the changed versus the familiar objects was used as a measure of recognition memory. For the object-identity-trained animals, spaced training produced clear evidence of recognition memory after both 24h and 7 days, but massed-training animals showed it only after 24h. In contrast, for the object-location-trained animals, recognition memory was evident after both retention intervals and with both training procedures. When objects were placed in variable locations for the two types of training and the test was done with a brand-new location, only the spaced-training animals showed recognition at 24h, but surprisingly, after 7 days, animals trained using both procedures were able to recognize the change, suggesting a post-training consolidation process. We suggest that the two training procedures trigger different neural mechanisms that may differ in the two segregated streams that process object information and that may consolidate differently.

  9. Differential effects of spaced vs. massed training in long-term object-identity and object-location recognition memory.

    PubMed

    Bello-Medina, Paola C; Sánchez-Carrasco, Livia; González-Ornelas, Nadia R; Jeffery, Kathryn J; Ramírez-Amaya, Víctor

    2013-08-01

    Here we tested whether the well-known superiority of spaced training over massed training is equally evident in both object identity and object location recognition memory. We trained animals with objects placed in a variable or in a fixed location to produce a location-independent object identity memory or a location-dependent object representation. The training consisted of 5 trials that occurred either on one day (Massed) or over the course of 5 consecutive days (Spaced). The memory test was done in independent groups of animals either 24h or 7 days after the last training trial. In each test the animals were exposed to either a novel object, when trained with the objects in variable locations, or to a familiar object in a novel location, when trained with objects in fixed locations. The difference in time spent exploring the changed versus the familiar objects was used as a measure of recognition memory. For the object-identity-trained animals, spaced training produced clear evidence of recognition memory after both 24h and 7 days, but massed-training animals showed it only after 24h. In contrast, for the object-location-trained animals, recognition memory was evident after both retention intervals and with both training procedures. When objects were placed in variable locations for the two types of training and the test was done with a brand-new location, only the spaced-training animals showed recognition at 24h, but surprisingly, after 7 days, animals trained using both procedures were able to recognize the change, suggesting a post-training consolidation process. We suggest that the two training procedures trigger different neural mechanisms that may differ in the two segregated streams that process object information and that may consolidate differently. PMID:23644160

  10. Recognition of Single and Overlay of Objects on a Conveyor Belt

    NASA Astrophysics Data System (ADS)

    Savicheva, S. V.

    2015-05-01

    Proposed a method for detection of flat objects when they overlap condition. The method is based on two separate recognition algorithms flat objects. The first algorithm is based on a binary image of the signature of the object plane. The second algorithm is based on the values of the discrete points in the curvature contour of a binary image. The results of experimental studies of algorithms and a method of recognition of individual superimposed flat objects.

  11. Toward the ultimate synthesis/recognition system.

    PubMed Central

    Furui, S

    1995-01-01

    This paper predicts speech synthesis, speech recognition, and speaker recognition technology for the year 2001, and it describes the most important research problems to be solved in order to arrive at these ultimate synthesis and recognition systems. The problems for speech synthesis include natural and intelligible voice production, prosody control based on meaning, capability of controlling synthesized voice quality and choosing individual speaking style, multilingual and multidialectal synthesis, choice of application-oriented speaking styles, capability of adding emotion, and synthesis from concepts. The problems for speech recognition include robust recognition against speech variations, adaptation/normalization to variations due to environmental conditions and speakers, automatic knowledge acquisition for acoustic and linguistic modeling, spontaneous speech recognition, naturalness and ease of human-machine interaction, and recognition of emotion. The problems for speaker recognition are similar to those for speech recognition. The research topics related to all these techniques include the use of articulatory and perceptual constraints and evaluation methods for measuring the quality of technology and systems. Images Fig. 3 PMID:7479723

  12. Parts and Relations in Young Children's Shape-Based Object Recognition

    ERIC Educational Resources Information Center

    Augustine, Elaine; Smith, Linda B.; Jones, Susan S.

    2011-01-01

    The ability to recognize common objects from sparse information about geometric shape emerges during the same period in which children learn object names and object categories. Hummel and Biederman's (1992) theory of object recognition proposes that the geometric shapes of objects have two components--geometric volumes representing major object…

  13. RecceMan: an interactive recognition assistance for image-based reconnaissance: synergistic effects of human perception and computational methods for object recognition, identification, and infrastructure analysis

    NASA Astrophysics Data System (ADS)

    El Bekri, Nadia; Angele, Susanne; Ruckhäberle, Martin; Peinsipp-Byma, Elisabeth; Haelke, Bruno

    2015-10-01

    This paper introduces an interactive recognition assistance system for imaging reconnaissance. This system supports aerial image analysts on missions during two main tasks: Object recognition and infrastructure analysis. Object recognition concentrates on the classification of one single object. Infrastructure analysis deals with the description of the components of an infrastructure and the recognition of the infrastructure type (e.g. military airfield). Based on satellite or aerial images, aerial image analysts are able to extract single object features and thereby recognize different object types. It is one of the most challenging tasks in the imaging reconnaissance. Currently, there are no high potential ATR (automatic target recognition) applications available, as consequence the human observer cannot be replaced entirely. State-of-the-art ATR applications cannot assume in equal measure human perception and interpretation. Why is this still such a critical issue? First, cluttered and noisy images make it difficult to automatically extract, classify and identify object types. Second, due to the changed warfare and the rise of asymmetric threats it is nearly impossible to create an underlying data set containing all features, objects or infrastructure types. Many other reasons like environmental parameters or aspect angles compound the application of ATR supplementary. Due to the lack of suitable ATR procedures, the human factor is still important and so far irreplaceable. In order to use the potential benefits of the human perception and computational methods in a synergistic way, both are unified in an interactive assistance system. RecceMan® (Reconnaissance Manual) offers two different modes for aerial image analysts on missions: the object recognition mode and the infrastructure analysis mode. The aim of the object recognition mode is to recognize a certain object type based on the object features that originated from the image signatures. The

  14. Three-dimensional object representation and invariant recognition using continuous distance transform neural networks.

    PubMed

    Tseng, Y H; Hwang, J N; Sheehan, F H

    1997-01-01

    3D object recognition under partial object viewing is a difficult pattern recognition task. In this paper, we introduce a neural-network solution that is robust to partial viewing of objects and noise corruption. This method directly utilizes the acquired 3D data and requires no feature extraction. The object is first parametrically represented by a continuous distance transform neural network (CDTNN) trained by the surface points of the exemplar object. The CDTNN maps any 3D coordinate into a value that corresponds to the distance between the point and the nearest surface point of the object. Therefore, a mismatch between the exemplar object and an unknown object can be easily computed. When encountered with deformed objects, this mismatch information can be backpropagated through the CDTNN to iteratively determine the deformation in terms of affine transform. Application to 3D heart contour delineation and invariant recognition of 3D rigid-body objects is presented.

  15. How Does Using Object Names Influence Visual Recognition Memory?

    ERIC Educational Resources Information Center

    Richler, Jennifer J.; Palmeri, Thomas J.; Gauthier, Isabel

    2013-01-01

    Two recent lines of research suggest that explicitly naming objects at study influences subsequent memory for those objects at test. Lupyan (2008) suggested that naming "impairs" memory by a representational shift of stored representations of named objects toward the prototype (labeling effect). MacLeod, Gopie, Hourihan, Neary, and Ozubko (2010)…

  16. Symbolic Play Connects to Language through Visual Object Recognition

    ERIC Educational Resources Information Center

    Smith, Linda B.; Jones, Susan S.

    2011-01-01

    Object substitutions in play (e.g. using a box as a car) are strongly linked to language learning and their absence is a diagnostic marker of language delay. Classic accounts posit a symbolic function that underlies both words and object substitutions. Here we show that object substitutions depend on developmental changes in visual object…

  17. Higher-order neural network software for distortion invariant object recognition

    NASA Technical Reports Server (NTRS)

    Reid, Max B.; Spirkovska, Lilly

    1991-01-01

    The state-of-the-art in pattern recognition for such applications as automatic target recognition and industrial robotic vision relies on digital image processing. We present a higher-order neural network model and software which performs the complete feature extraction-pattern classification paradigm required for automatic pattern recognition. Using a third-order neural network, we demonstrate complete, 100 percent accurate invariance to distortions of scale, position, and in-plate rotation. In a higher-order neural network, feature extraction is built into the network, and does not have to be learned. Only the relatively simple classification step must be learned. This is key to achieving very rapid training. The training set is much smaller than with standard neural network software because the higher-order network only has to be shown one view of each object to be learned, not every possible view. The software and graphical user interface run on any Sun workstation. Results of the use of the neural software in autonomous robotic vision systems are presented. Such a system could have extensive application in robotic manufacturing.

  18. The role of sensory-motor information in object recognition: evidence from category-specific visual agnosia.

    PubMed

    Wolk, David A; Coslett, H Branch; Glosser, Guila

    2005-08-01

    The role of sensory-motor representations in object recognition was investigated in experiments involving AD, a patient with mild visual agnosia who was impaired in the recognition of visually presented living as compared to non-living entities. AD named visually presented items for which sensory-motor information was available significantly more reliably than items for which such information was not available; this was true when all items were non-living. Naming of objects from their associated sound was normal. These data suggest that both information about object form computed in the ventral visual system as well as sensory-motor information specifying the manner of manipulation contribute to object recognition.

  19. Support plane method applied to ground objects recognition using modelled SAR images

    NASA Astrophysics Data System (ADS)

    Zherdev, Denis A.; Fursov, Vladimir A.

    2015-09-01

    In this study, the object recognition problem was solved using support plane method. The modelled SAR images were used as features vectors in the recognition algorithm. Radar signal backscattering of objects in different observing poses is presented in SAR images. For real time simulation, we used simple mixture model of Lambertian-specular reflectivity. To this end, an algorithm of ray tracing is extended for simulating SAR images of 3D man-made models. The suggested algorithm of support plane is very effective in objects recognition using SAR images and RCS diagrams.

  20. Resolving human object recognition in space and time.

    PubMed

    Cichy, Radoslaw Martin; Pantazis, Dimitrios; Oliva, Aude

    2014-03-01

    A comprehensive picture of object processing in the human brain requires combining both spatial and temporal information about brain activity. Here we acquired human magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) responses to 92 object images. Multivariate pattern classification applied to MEG revealed the time course of object processing: whereas individual images were discriminated by visual representations early, ordinate and superordinate category levels emerged relatively late. Using representational similarity analysis, we combined human fMRI and MEG to show content-specific correspondence between early MEG responses and primary visual cortex (V1), and later MEG responses and inferior temporal (IT) cortex. We identified transient and persistent neural activities during object processing with sources in V1 and IT. Finally, we correlated human MEG signals to single-unit responses in monkey IT. Together, our findings provide an integrated space- and time-resolved view of human object categorization during the first few hundred milliseconds of vision.

  1. Resolving human object recognition in space and time

    PubMed Central

    Cichy, Radoslaw Martin; Pantazis, Dimitrios; Oliva, Aude

    2014-01-01

    A comprehensive picture of object processing in the human brain requires combining both spatial and temporal information about brain activity. Here, we acquired human magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) responses to 92 object images. Multivariate pattern classification applied to MEG revealed the time course of object processing: whereas individual images were discriminated by visual representations early, ordinate and superordinate category levels emerged relatively later. Using representational similarity analysis, we combine human fMRI and MEG to show content-specific correspondence between early MEG responses and primary visual cortex (V1), and later MEG responses and inferior temporal (IT) cortex. We identified transient and persistent neural activities during object processing, with sources in V1 and IT., Finally, human MEG signals were correlated to single-unit responses in monkey IT. Together, our findings provide an integrated space- and time-resolved view of human object categorization during the first few hundred milliseconds of vision. PMID:24464044

  2. Distinct roles of basal forebrain cholinergic neurons in spatial and object recognition memory.

    PubMed

    Okada, Kana; Nishizawa, Kayo; Kobayashi, Tomoko; Sakata, Shogo; Kobayashi, Kazuto

    2015-08-06

    Recognition memory requires processing of various types of information such as objects and locations. Impairment in recognition memory is a prominent feature of amnesia and a symptom of Alzheimer's disease (AD). Basal forebrain cholinergic neurons contain two major groups, one localized in the medial septum (MS)/vertical diagonal band of Broca (vDB), and the other in the nucleus basalis magnocellularis (NBM). The roles of these cell groups in recognition memory have been debated, and it remains unclear how they contribute to it. We use a genetic cell targeting technique to selectively eliminate cholinergic cell groups and then test spatial and object recognition memory through different behavioural tasks. Eliminating MS/vDB neurons impairs spatial but not object recognition memory in the reference and working memory tasks, whereas NBM elimination undermines only object recognition memory in the working memory task. These impairments are restored by treatment with acetylcholinesterase inhibitors, anti-dementia drugs for AD. Our results highlight that MS/vDB and NBM cholinergic neurons are not only implicated in recognition memory but also have essential roles in different types of recognition memory.

  3. Distinct roles of basal forebrain cholinergic neurons in spatial and object recognition memory

    PubMed Central

    Okada, Kana; Nishizawa, Kayo; Kobayashi, Tomoko; Sakata, Shogo; Kobayashi, Kazuto

    2015-01-01

    Recognition memory requires processing of various types of information such as objects and locations. Impairment in recognition memory is a prominent feature of amnesia and a symptom of Alzheimer’s disease (AD). Basal forebrain cholinergic neurons contain two major groups, one localized in the medial septum (MS)/vertical diagonal band of Broca (vDB), and the other in the nucleus basalis magnocellularis (NBM). The roles of these cell groups in recognition memory have been debated, and it remains unclear how they contribute to it. We use a genetic cell targeting technique to selectively eliminate cholinergic cell groups and then test spatial and object recognition memory through different behavioural tasks. Eliminating MS/vDB neurons impairs spatial but not object recognition memory in the reference and working memory tasks, whereas NBM elimination undermines only object recognition memory in the working memory task. These impairments are restored by treatment with acetylcholinesterase inhibitors, anti-dementia drugs for AD. Our results highlight that MS/vDB and NBM cholinergic neurons are not only implicated in recognition memory but also have essential roles in different types of recognition memory. PMID:26246157

  4. Automatic TLI recognition system, general description

    SciTech Connect

    Lassahn, G.D.

    1997-02-01

    This report is a general description of an automatic target recognition system developed at the Idaho National Engineering Laboratory for the Department of Energy. A user`s manual is a separate volume, Automatic TLI Recognition System, User`s Guide, and a programmer`s manual is Automatic TLI Recognition System, Programmer`s Guide. This system was designed as an automatic target recognition system for fast screening of large amounts of multi-sensor image data, based on low-cost parallel processors. This system naturally incorporates image data fusion, and it gives uncertainty estimates. It is relatively low cost, compact, and transportable. The software is easily enhanced to expand the system`s capabilities, and the hardware is easily expandable to increase the system`s speed. In addition to its primary function as a trainable target recognition system, this is also a versatile, general-purpose tool for image manipulation and analysis, which can be either keyboard-driven or script-driven. This report includes descriptions of three variants of the computer hardware, a description of the mathematical basis if the training process, and a description with examples of the system capabilities.

  5. ART-EMAP: A neural network architecture for object recognition by evidence accumulation.

    PubMed

    Carpenter, G A; Ross, W D

    1995-01-01

    A new neural network architecture is introduced for the recognition of pattern classes after supervised and unsupervised learning. Applications include spatio-temporal image understanding and prediction and 3D object recognition from a series of ambiguous 2D views. The architecture, called ART-EMAP, achieves a synthesis of adaptive resonance theory (ART) and spatial and temporal evidence integration for dynamic predictive mapping (EMAP). ART-EMAP extends the capabilities of fuzzy ARTMAP in four incremental stages. Stage 1 introduces distributed pattern representation at a view category field. Stage 2 adds a decision criterion to the mapping between view and object categories, delaying identification of ambiguous objects when faced with a low confidence prediction. Stage 3 augments the system with a field where evidence accumulates in medium-term memory. Stage 4 adds an unsupervised learning process to fine-tune performance after the limited initial period of supervised network training. Each ART-EMAP stage is illustrated with a benchmark simulation example, using both noisy and noise-free data. PMID:18263371

  6. It’s all connected: Pathways in visual object recognition and early noun learning

    PubMed Central

    Smith, Linda B.

    2013-01-01

    A developmental pathway may be defined as the route, or chain of events, through which a new structure or function forms. For many human behaviors, including object name learning and visual object recognition, these pathways are often complex, multi-causal and include unexpected dependencies. This paper presents three principles of development that suggest the value of a developmental psychology that explicitly seeks to trace these pathways and uses empirical evidence on developmental dependencies between motor development, action on objects, visual object recognition and object name learning in 12 to 24 month old infants to make the case. The paper concludes with a consideration of the theoretical implications of this approach. PMID:24320634

  7. Do Simultaneously Viewed Objects Influence Scene Recognition Individually or as Groups? Two Perceptual Studies

    PubMed Central

    Gagne, Christopher R.; MacEvoy, Sean P.

    2014-01-01

    The ability to quickly categorize visual scenes is critical to daily life, allowing us to identify our whereabouts and to navigate from one place to another. Rapid scene categorization relies heavily on the kinds of objects scenes contain; for instance, studies have shown that recognition is less accurate for scenes to which incongruent objects have been added, an effect usually interpreted as evidence of objects' general capacity to activate semantic networks for scene categories they are statistically associated with. Essentially all real-world scenes contain multiple objects, however, and it is unclear whether scene recognition draws on the scene associations of individual objects or of object groups. To test the hypothesis that scene recognition is steered, at least in part, by associations between object groups and scene categories, we asked observers to categorize briefly-viewed scenes appearing with object pairs that were semantically consistent or inconsistent with the scenes. In line with previous results, scenes were less accurately recognized when viewed with inconsistent versus consistent pairs. To understand whether this reflected individual or group-level object associations, we compared the impact of pairs composed of mutually related versus unrelated objects; i.e., pairs, which, as groups, had clear associations to particular scene categories versus those that did not. Although related and unrelated object pairs equally reduced scene recognition accuracy, unrelated pairs were consistently less capable of drawing erroneous scene judgments towards scene categories associated with their individual objects. This suggests that scene judgments were influenced by the scene associations of object groups, beyond the influence of individual objects. More generally, the fact that unrelated objects were as capable of degrading categorization accuracy as related objects, while less capable of generating specific alternative judgments, indicates that the process

  8. Kappa Opioid Receptor-Mediated Disruption of Novel Object Recognition: Relevance for Psychostimulant Treatment

    PubMed Central

    Paris, Jason J.; Reilley, Kate J.; McLaughlin, Jay P.

    2012-01-01

    Kappa opioid receptor (KOR) agonists are potentially valuable as therapeutics for the treatment of psychostimulant reward as they suppress dopamine signaling in reward circuitry to repress drug seeking behavior. However, KOR agonists are also associated with sedation and cognitive dysfunction. The extent to which learning and memory disruption or hypolocomotion underlie KOR agonists’ role in counteracting the rewarding effects of psychostimulants is of interest. C57BL/6J mice were pretreated with vehicle (saline, 0.9%), the KOR agonist (trans)-3,4-dichloro-N-methyl-N-[2-(1- pyrrolidinyl)-cyclohexyl] benzeneacetamide (U50,488), or the peripherally-restricted agonist D-Phe-D-Phe-D-lle-D-Arg- NH2 (ffir-NH2), through central (i.c.v.) or peripheral (i.p.) routes of administration. Locomotor activity was assessed via activity monitoring chambers and rotorod. Cognitive performance was assessed in a novel object recognition task. Prolonged hypolocomotion was observed following administration of 1.0 and 10.0, but not 0.3 mg/kg U50,488. Central, but not peripheral, administration of ffir-NH2 (a KOR agonist that does not cross the blood-brain barrier) also reduced motor behavior. Systemic pretreatment with the low dose of U50,488 (0.3 mg/kg, i.p.) significantly impaired performance in the novel object recognition task. Likewise, ffir-NH2 significantly reduced novel object recognition after central (i.c.v.), but not peripheral (i.p.), administration. U50,488- and ffir-NH2-mediated deficits in novel object recognition were prevented by pretreatment with KOR antagonists. Cocaine-induced conditioned place preference was subsequently assessed and was reduced by pretreatment with U50,488 (0.3 mg/kg, i.p.). Together, these results suggest that the activation of centrally-located kappa opioid receptors may induce cognitive and mnemonic disruption independent of hypolocomotor effects which may contribute to the KOR-mediated suppression of psychostimulant reward. PMID:22900234

  9. Complementary Hemispheric Asymmetries in Object Naming and Recognition: A Voxel-Based Correlational Study

    ERIC Educational Resources Information Center

    Acres, K.; Taylor, K. I.; Moss, H. E.; Stamatakis, E. A.; Tyler, L. K.

    2009-01-01

    Cognitive neuroscientific research proposes complementary hemispheric asymmetries in naming and recognising visual objects, with a left temporal lobe advantage for object naming and a right temporal lobe advantage for object recognition. Specifically, it has been proposed that the left inferior temporal lobe plays a mediational role linking…

  10. Priming Contour-Deleted Images: Evidence for Immediate Representations in Visual Object Recognition.

    ERIC Educational Resources Information Center

    Biederman, Irving; Cooper, Eric E.

    1991-01-01

    Speed and accuracy of identification of pictures of objects are facilitated by prior viewing. Contributions of image features, convex or concave components, and object models in a repetition priming task were explored in 2 studies involving 96 college students. Results provide evidence of intermediate representations in visual object recognition.…

  11. Dissociating the Effects of Angular Disparity and Image Similarity in Mental Rotation and Object Recognition

    ERIC Educational Resources Information Center

    Cheung, Olivia S.; Hayward, William G.; Gauthier, Isabel

    2009-01-01

    Performance is often impaired linearly with increasing angular disparity between two objects in tasks that measure mental rotation or object recognition. But increased angular disparity is often accompanied by changes in the similarity between views of an object, confounding the impact of the two factors in these tasks. We examined separately the…

  12. The relationship between change detection and recognition of centrally attended objects in motion pictures.

    PubMed

    Angelone, Bonnie L; Levin, Daniel T; Simons, Daniel J

    2003-01-01

    Observers typically detect changes to central objects more readily than changes to marginal objects, but they sometimes miss changes to central, attended objects as well. However, even if observers do not report such changes, they may be able to recognize the changed object. In three experiments we explored change detection and recognition memory for several types of changes to central objects in motion pictures. Observers who failed to detect a change still performed at above chance levels on a recognition task in almost all conditions. In addition, observers who detected the change were no more accurate in their recognition than those who did not detect the change. Despite large differences in the detectability of changes across conditions, those observers who missed the change did not vary in their ability to recognize the changing object.

  13. Formal Implementation of a Performance Evaluation Model for the Face Recognition System

    PubMed Central

    Shin, Yong-Nyuo; Kim, Jason; Lee, Yong-Jun; Shin, Woochang; Choi, Jin-Young

    2008-01-01

    Due to usability features, practical applications, and its lack of intrusiveness, face recognition technology, based on information, derived from individuals' facial features, has been attracting considerable attention recently. Reported recognition rates of commercialized face recognition systems cannot be admitted as official recognition rates, as they are based on assumptions that are beneficial to the specific system and face database. Therefore, performance evaluation methods and tools are necessary to objectively measure the accuracy and performance of any face recognition system. In this paper, we propose and formalize a performance evaluation model for the biometric recognition system, implementing an evaluation tool for face recognition systems based on the proposed model. Furthermore, we performed evaluations objectively by providing guidelines for the design and implementation of a performance evaluation system, formalizing the performance test process. PMID:18317524

  14. An optimal sensing strategy for recognition and localization of 3-D natural quadric objects

    NASA Technical Reports Server (NTRS)

    Lee, Sukhan; Hahn, Hernsoo

    1991-01-01

    An optimal sensing strategy for an optical proximity sensor system engaged in the recognition and localization of 3-D natural quadric objects is presented. The optimal sensing strategy consists of the selection of an optimal beam orientation and the determination of an optimal probing plane that compose an optimal data collection operation known as an optimal probing. The decision of an optimal probing is based on the measure of discrimination power of a cluster of surfaces on a multiple interpretation image (MII), where the measure of discrimination power is defined in terms of a utility function computing the expected number of interpretations that can be pruned out by a probing. An object representation suitable for active sensing based on a surface description vector (SDV) distribution graph and hierarchical tables is presented. Experimental results are shown.

  15. Automatic TLI recognition system beta prototype testing

    SciTech Connect

    Lassahn, G.D.

    1996-06-01

    This report describes the beta prototype automatic target recognition system ATR3, and some performance tests done with this system. This is a fully operational system, with a high computational speed. It is useful for findings any kind of target in digitized image data, and as a general purpose image analysis tool.

  16. Zero-Copy Objects System

    NASA Technical Reports Server (NTRS)

    Burleigh, Scott C.

    2011-01-01

    Zero-Copy Objects System software enables application data to be encapsulated in layers of communication protocol without being copied. Indirect referencing enables application source data, either in memory or in a file, to be encapsulated in place within an unlimited number of protocol headers and/or trailers. Zero-copy objects (ZCOs) are abstract data access representations designed to minimize I/O (input/output) in the encapsulation of application source data within one or more layers of communication protocol structure. They are constructed within the heap space of a Simple Data Recorder (SDR) data store to which all participating layers of the stack must have access. Each ZCO contains general information enabling access to the core source data object (an item of application data), together with (a) a linked list of zero or more specific extents that reference portions of this source data object, and (b) linked lists of protocol header and trailer capsules. The concatenation of the headers (in ascending stack sequence), the source data object extents, and the trailers (in descending stack sequence) constitute the transmitted data object constructed from the ZCO. This scheme enables a source data object to be encapsulated in a succession of protocol layers without ever having to be copied from a buffer at one layer of the protocol stack to an encapsulating buffer at a lower layer of the stack. For large source data objects, the savings in copy time and reduction in memory consumption may be considerable.

  17. Single prolonged stress impairs social and object novelty recognition in rats

    PubMed Central

    Eagle, Andrew L.; Fitzpatrick, Chris J.; Perrine, Shane A.

    2013-01-01

    Posttraumatic stress disorder (PTSD) results from exposure to a traumatic event and manifests as re-experiencing, arousal, avoidance, and negative cognition/mood symptoms. Avoidant symptoms, as well as the newly defined negative cognitions/mood, are a serious complication leading to diminished interest in once important or positive activities, such as social interaction; however, the basis of these symptoms remains poorly understood. PTSD patients also exhibit impaired object and social recognition, which may underlie the avoidance and symptoms of negative cognition, such as social estrangement or diminished interest in activities. Previous studies have demonstrated that single prolonged stress (SPS), models PTSD phenotypes, including impairments in learning and memory. Therefore, it was hypothesized that SPS would impair social and object recognition memory. Male Sprague Dawley rats were exposed to SPS then tested in the social choice test (SCT) or novel object recognition test (NOR). These tests measure recognition of novelty over familiarity, a natural preference of rodents. Results show that SPS impaired preference for both social and object novelty. In addition, SPS impairment in social recognition may be caused by impaired behavioral flexibility, or an inability to shift behavior during the SCT. These results demonstrate that traumatic stress can impair social and object recognition memory, which may underlie certain avoidant symptoms or negative cognition in PTSD and be related to impaired behavioral flexibility. PMID:24036168

  18. Feature extraction and object recognition in multi-modal forward looking imagery

    NASA Astrophysics Data System (ADS)

    Greenwood, G.; Blakely, S.; Schartman, D.; Calhoun, B.; Keller, J. M.; Ton, T.; Wong, D.; Soumekh, M.

    2010-04-01

    The U. S. Army Night Vision and Electronic Sensors Directorate (NVESD) recently tested an explosive-hazards detection vehicle that combines a pulsed FLGPR with a visible-spectrum color camera. Additionally, NVESD tested a human-in-the-loop multi-camera system with the same goal in mind. It contains wide field-of-view color and infrared cameras as well as zoomable narrow field-of-view versions of those modalities. Even though they are separate vehicles, having information from both systems offers great potential for information fusion. Based on previous work at the University of Missouri, we are not only able to register the UTM-based positions of the FLGPR to the color image sequences on the first system, but we can register these locations to corresponding image frames of all sensors on the human-in-the-loop platform. This paper presents our approach to first generate libraries of multi-sensor information across these platforms. Subsequently, research is performed in feature extraction and recognition algorithms based on the multi-sensor signatures. Our goal is to tailor specific algorithms to recognize and eliminate different categories of clutter and to be able to identify particular explosive hazards. We demonstrate our library creation, feature extraction and object recognition results on a large data collection at a US Army test site.

  19. Probabilistic 3D object recognition and pose estimation using multiple interpretations generation.

    PubMed

    Lu, Zhaojin; Lee, Sukhan

    2011-12-01

    This paper presents a probabilistic object recognition and pose estimation method using multiple interpretation generation in cluttered indoor environments. How to handle pose ambiguity and uncertainty is the main challenge in most recognition systems. In order to solve this problem, we approach it in a probabilistic manner. First, given a three-dimensional (3D) polyhedral object model, the parallel and perpendicular line pairs, which are detected from stereo images and 3D point clouds, generate pose hypotheses as multiple interpretations, with ambiguity from partial occlusion and fragmentation of 3D lines especially taken into account. Different from the previous methods, each pose interpretation is represented as a region instead of a point in pose space reflecting the measurement uncertainty. Then, for each pose interpretation, more features around the estimated pose are further utilized as additional evidence for computing the probability using the Bayesian principle in terms of likelihood and unlikelihood. Finally, fusion strategy is applied to the top ranked interpretations with high probabilities, which are further verified and refined to give a more accurate pose estimation in real time. The experimental results show the performance and potential of the proposed approach in real cluttered domestic environments.

  20. Speech recognition system for an automotive vehicle

    SciTech Connect

    Noso, K.; Futami, T.

    1987-01-13

    A speech recognition system is described for an automotive vehicle for activating vehicle actuators in response to predetermined spoken instructions supplied to the system via a microphone, which comprises: (a) a manually controlled record switch for deriving a record signal when activated; (b) a manually controlled recognition switch for deriving a recognition signal when activated; (c) a speech recognizer for sequentially recording reference spoken instructions whenever one reference spoken instruction is supplied to the system through the microphone while the record switch is activated, a memory having a storage area for each spoken instruction, and means for shifting access to each storage area for each spoken instruction has been recorded in the storage area provided therefore. A means is included for activating vehicle actuators sequentially whenever one recognition spoken instruction is supplied to the system via the microphone while the recognition switch is activated and when the spoken instruction to be recognized is similar to the reference spoken instruction; and (d) means for deriving skip instruction signal and for coupling the skip instruction signal to the speech recognizer to shift access from a currently accessed storage area for recording a current reference spoken instruction to a succeeding storage area for recording a succeeding reference spoken instruction even when the current reference spoken instruction is not supplied to the system through the microphone.

  1. Multi-class remote sensing object recognition based on discriminative sparse representation.

    PubMed

    Wang, Xin; Shen, Siqiu; Ning, Chen; Huang, Fengchen; Gao, Hongmin

    2016-02-20

    The automatic recognition of multi-class objects with various backgrounds is a big challenge in the field of remote sensing (RS) image analysis. In this paper, we propose a novel recognition framework for multi-class RS objects based on the discriminative sparse representation. In this framework, the recognition problem is implemented in two stages. In the first, or discriminative dictionary learning stage, considering the characterization of remote sensing objects, the scale-invariant feature transform descriptor is first combined with an improved bag-of-words model for multi-class objects feature extraction and representation. Then, information about each class of training samples is fused into the dictionary learning process; by using the K-singular value decomposition algorithm, a discriminative dictionary can be learned for sparse coding. In the second, or recognition, stage, to improve the computational efficiency, the phase spectrum of a quaternion Fourier transform model is applied to the test image to predict a small set of object candidate locations. Then, a multi-scale sliding window mechanism is utilized to scan the image over those candidate locations to obtain the object candidates (or objects of interest). Subsequently, the sparse coding coefficients of these candidates under the discriminative dictionary are mapped to the discriminative vectors that have a good ability to distinguish different classes of objects. Finally, multi-class object recognition can be accomplished by analyzing these vectors. The experimental results show that the proposed work outperforms a number of state-of-the-art methods for multi-class remote sensing object recognition.

  2. Multi-class remote sensing object recognition based on discriminative sparse representation.

    PubMed

    Wang, Xin; Shen, Siqiu; Ning, Chen; Huang, Fengchen; Gao, Hongmin

    2016-02-20

    The automatic recognition of multi-class objects with various backgrounds is a big challenge in the field of remote sensing (RS) image analysis. In this paper, we propose a novel recognition framework for multi-class RS objects based on the discriminative sparse representation. In this framework, the recognition problem is implemented in two stages. In the first, or discriminative dictionary learning stage, considering the characterization of remote sensing objects, the scale-invariant feature transform descriptor is first combined with an improved bag-of-words model for multi-class objects feature extraction and representation. Then, information about each class of training samples is fused into the dictionary learning process; by using the K-singular value decomposition algorithm, a discriminative dictionary can be learned for sparse coding. In the second, or recognition, stage, to improve the computational efficiency, the phase spectrum of a quaternion Fourier transform model is applied to the test image to predict a small set of object candidate locations. Then, a multi-scale sliding window mechanism is utilized to scan the image over those candidate locations to obtain the object candidates (or objects of interest). Subsequently, the sparse coding coefficients of these candidates under the discriminative dictionary are mapped to the discriminative vectors that have a good ability to distinguish different classes of objects. Finally, multi-class object recognition can be accomplished by analyzing these vectors. The experimental results show that the proposed work outperforms a number of state-of-the-art methods for multi-class remote sensing object recognition. PMID:26906591

  3. Stochastic Process Underlying Emergent Recognition of Visual Objects Hidden in Degraded Images

    PubMed Central

    Murata, Tsutomu; Hamada, Takashi; Shimokawa, Tetsuya; Tanifuji, Manabu; Yanagida, Toshio

    2014-01-01

    When a degraded two-tone image such as a “Mooney” image is seen for the first time, it is unrecognizable in the initial seconds. The recognition of such an image is facilitated by giving prior information on the object, which is known as top-down facilitation and has been intensively studied. Even in the absence of any prior information, however, we experience sudden perception of the emergence of a salient object after continued observation of the image, whose processes remain poorly understood. This emergent recognition is characterized by a comparatively long reaction time ranging from seconds to tens of seconds. In this study, to explore this time-consuming process of emergent recognition, we investigated the properties of the reaction times for recognition of degraded images of various objects. The results show that the time-consuming component of the reaction times follows a specific exponential function related to levels of image degradation and subject's capability. Because generally an exponential time is required for multiple stochastic events to co-occur, we constructed a descriptive mathematical model inspired by the neurophysiological idea of combination coding of visual objects. Our model assumed that the coincidence of stochastic events complement the information loss of a degraded image leading to the recognition of its hidden object, which could successfully explain the experimental results. Furthermore, to see whether the present results are specific to the task of emergent recognition, we also conducted a comparison experiment with the task of perceptual decision making of degraded images, which is well known to be modeled by the stochastic diffusion process. The results indicate that the exponential dependence on the level of image degradation is specific to emergent recognition. The present study suggests that emergent recognition is caused by the underlying stochastic process which is based on the coincidence of multiple stochastic events

  4. From neural-based object recognition toward microelectronic eyes

    NASA Technical Reports Server (NTRS)

    Sheu, Bing J.; Bang, Sa Hyun

    1994-01-01

    Engineering neural network systems are best known for their abilities to adapt to the changing characteristics of the surrounding environment by adjusting system parameter values during the learning process. Rapid advances in analog current-mode design techniques have made possible the implementation of major neural network functions in custom VLSI chips. An electrically programmable analog synapse cell with large dynamic range can be realized in a compact silicon area. New designs of the synapse cells, neurons, and analog processor are presented. A synapse cell based on Gilbert multiplier structure can perform the linear multiplication for back-propagation networks. A double differential-pair synapse cell can perform the Gaussian function for radial-basis network. The synapse cells can be biased in the strong inversion region for high-speed operation or biased in the subthreshold region for low-power operation. The voltage gain of the sigmoid-function neurons is externally adjustable which greatly facilitates the search of optimal solutions in certain networks. Various building blocks can be intelligently connected to form useful industrial applications. Efficient data communication is a key system-level design issue for large-scale networks. We also present analog neural processors based on perceptron architecture and Hopfield network for communication applications. Biologically inspired neural networks have played an important role towards the creation of powerful intelligent machines. Accuracy, limitations, and prospects of analog current-mode design of the biologically inspired vision processing chips and cellular neural network chips are key design issues.

  5. Scalable Medical Image Understanding by Fusing Cross-Modal Object Recognition with Formal Domain Semantics

    NASA Astrophysics Data System (ADS)

    Möller, Manuel; Sintek, Michael; Buitelaar, Paul; Mukherjee, Saikat; Zhou, Xiang Sean; Freund, Jörg

    Recent advances in medical imaging technology have dramatically increased the amount of clinical image data. In contrast, techniques for efficiently exploiting the rich semantic information in medical images have evolved much slower. Despite the research outcomes in image understanding, current image databases are still indexed by manually assigned subjective keywords instead of the semantics of the images. Indeed, most current content-based image search applications index image features that do not generalize well and use inflexible queries. This slow progress is due to the lack of scalable and generic information representation systems which can abstract over the high dimensional nature of medical images as well as semantically model the results of object recognition techniques. We propose a system combining medical imaging information with ontological formalized semantic knowledge that provides a basis for building universal knowledge repositories and gives clinicians fully cross-lingual and cross-modal access to biomedical information.

  6. Expertise modulates the neural basis of context dependent recognition of objects and their relations.

    PubMed

    Bilalić, Merim; Turella, Luca; Campitelli, Guillermo; Erb, Michael; Grodd, Wolfgang

    2012-11-01

    Recognition of objects and their relations is necessary for orienting in real life. We examined cognitive processes related to recognition of objects, their relations, and the patterns they form by using the game of chess. Chess enables us to compare experts with novices and thus gain insight in the nature of development of recognition skills. Eye movement recordings showed that experts were generally faster than novices on a task that required enumeration of relations between chess objects because their extensive knowledge enabled them to immediately focus on the objects of interest. The advantage was less pronounced on random positions where the location of chess objects, and thus typical relations between them, was randomized. Neuroimaging data related experts' superior performance to the areas along the dorsal stream-bilateral posterior temporal areas and left inferior parietal lobe were related to recognition of object and their functions. The bilateral collateral sulci, together with bilateral retrosplenial cortex, were also more sensitive to normal than random positions among experts indicating their involvement in pattern recognition. The pattern of activations suggests experts engage the same regions as novices, but also that they employ novel additional regions. Expert processing, as the final stage of development, is qualitatively different than novice processing, which can be viewed as the starting stage. Since we are all experts in real life and dealing with meaningful stimuli in typical contexts, our results underline the importance of expert-like cognitive processing on generalization of laboratory results to everyday life. PMID:21998070

  7. A neuromorphic system for object detection and classification

    NASA Astrophysics Data System (ADS)

    Khosla, Deepak; Chen, Yang; Kim, Kyungnam; Cheng, Shinko Y.; Honda, Alexander L.; Zhang, Lei

    2013-05-01

    Unattended object detection, recognition and tracking on unmanned reconnaissance platforms in battlefields and urban spaces are topics of emerging importance. In this paper, we present an unattended object recognition system that automatically detects objects of interest in videos and classifies them into various categories (e.g., person, car, truck, etc.). Our system is inspired by recent findings in visual neuroscience on feed-forward object detection and recognition pipeline and mirrors that via two main neuromorphic modules (1) A front-end detection module that combines form and motion based visual attention to search for and detect "integrated" object percepts as is hypothesized to occur in the human visual pathways; (2) A back-end recognition module that processes only the detected object percepts through a neuromorphic object classification algorithm based on multi-scale convolutional neural networks, which can be efficiently implemented in COTS hardware. Our neuromorphic system was evaluated using a variety of urban area video data collected from both stationary and moving platforms. The data are quite challenging as it includes targets at long ranges, occurring under variable conditions of illuminations and occlusion with high clutter. The experimental results of our system showed excellent detection and classification performance. In addition, the proposed bio-inspired approach is good for hardware implementation due to its low complexity and mapping to off-the-shelf conventional hardware.

  8. License Plate Recognition System for Indian Vehicles

    NASA Astrophysics Data System (ADS)

    Sanap, P. R.; Narote, S. P.

    2010-11-01

    We consider the task of recognition of Indian vehicle number plates (also called license plates or registration plates in other countries). A system for Indian number plate recognition must cope with wide variations in the appearance of the plates. Each state uses its own range of designs with font variations between the designs. Also, vehicle owners may place the plates inside glass covered frames or use plates made of nonstandard materials. These issues compound the complexity of automatic number plate recognition, making existing approaches inadequate. We have developed a system that incorporates a novel combination of image processing and artificial neural network technologies to successfully locate and read Indian vehicle number plates in digital images. Commercial application of the system is envisaged.

  9. Changes in Visual Object Recognition Precede the Shape Bias in Early Noun Learning

    PubMed Central

    Yee, Meagan; Jones, Susan S.; Smith, Linda B.

    2012-01-01

    Two of the most formidable skills that characterize human beings are language and our prowess in visual object recognition. They may also be developmentally intertwined. Two experiments, a large sample cross-sectional study and a smaller sample 6-month longitudinal study of 18- to 24-month-olds, tested a hypothesized developmental link between changes in visual object representation and noun learning. Previous findings in visual object recognition indicate that children’s ability to recognize common basic level categories from sparse structural shape representations of object shape emerges between the ages of 18 and 24 months, is related to noun vocabulary size, and is lacking in children with language delay. Other research shows in artificial noun learning tasks that during this same developmental period, young children systematically generalize object names by shape, that this shape bias predicts future noun learning, and is lacking in children with language delay. The two experiments examine the developmental relation between visual object recognition and the shape bias for the first time. The results show that developmental changes in visual object recognition systematically precede the emergence of the shape bias. The results suggest a developmental pathway in which early changes in visual object recognition that are themselves linked to category learning enable the discovery of higher-order regularities in category structure and thus the shape bias in novel noun learning tasks. The proposed developmental pathway has implications for understanding the role of specific experience in the development of both visual object recognition and the shape bias in early noun learning. PMID:23227015

  10. Two-dimensional object recognition through two-stage string matching.

    PubMed

    Wu, W Y; Wang, M J

    1999-01-01

    A two-stage string matching method for the recognition of two-dimensional (2-D) objects is proposed in this work. The first stage is a global cyclic string matching. The second stage is a local matching with local dissimilarity measure computing. The dissimilarity measure function of the input shape and the reference shape are obtained by combining the global matching cost and the local dissimilarity measure. The proposed method has the advantage that there is no need to set any parameter in the recognition process. Experimental results indicate that the hostage string matching approach significantly improves the recognition rates compared to the one-stage string matching method. PMID:18267511

  11. Exceptional Solar-System Objects

    NASA Astrophysics Data System (ADS)

    Zellner, Benjamin

    1990-12-01

    This is a target-of-opportunity proposal for HST observations to be executed if a previously unknown, truly exceptional solar-system object or phenomenon is discovered either in the normal course of HST work or by anyone, anywhere. Trails due to unknown moving objects will often appear on HST images made for other purposes. A short trail seen near the opposition point or at high ecliptic latitude could represent a major addition to our knowledge of the solar system. Thus we further propose that all short trials seen on HST images taken in favorable regions of the sky be given a quick analysis in the Observation Support System for their possible significance. If an unusual object is found we propose to: (1) Seek from the owner of data rights permission to proceed as may be appropriate; (2) Contact the Minor Planet Center for an evaluation of the significance of the discovery; and (3) For an object that appears to be of great significance where effective groundbased followup appears unlikely, request the HST schedule be replanned for followup images and physical studies using HST.

  12. Young Children's Self-Generated Object Views and Object Recognition

    ERIC Educational Resources Information Center

    James, Karin H.; Jones, Susan S.; Smith, Linda B.; Swain, Shelley N.

    2014-01-01

    Two important and related developments in children between 18 and 24 months of age are the rapid expansion of object name vocabularies and the emergence of an ability to recognize objects from sparse representations of their geometric shapes. In the same period, children also begin to show a preference for planar views (i.e., views of objects held…

  13. Crowded and Sparse Domains in Object Recognition: Consequences for Categorization and Naming

    ERIC Educational Resources Information Center

    Gale, Tim M.; Laws, Keith R.; Foley, Kerry

    2006-01-01

    Some models of object recognition propose that items from structurally crowded categories (e.g., living things) permit faster access to superordinate semantic information than structurally dissimilar categories (e.g., nonliving things), but slower access to individual object information when naming items. We present four experiments that utilize…

  14. Modeling guidance and recognition in categorical search: Bridging human and computer object detection

    PubMed Central

    Zelinsky, Gregory J.; Peng, Yifan; Berg, Alexander C.; Samaras, Dimitris

    2013-01-01

    Search is commonly described as a repeating cycle of guidance to target-like objects, followed by the recognition of these objects as targets or distractors. Are these indeed separate processes using different visual features? We addressed this question by comparing observer behavior to that of support vector machine (SVM) models trained on guidance and recognition tasks. Observers searched for a categorically defined teddy bear target in four-object arrays. Target-absent trials consisted of random category distractors rated in their visual similarity to teddy bears. Guidance, quantified as first-fixated objects during search, was strongest for targets, followed by target-similar, medium-similarity, and target-dissimilar distractors. False positive errors to first-fixated distractors also decreased with increasing dissimilarity to the target category. To model guidance, nine teddy bear detectors, using features ranging in biological plausibility, were trained on unblurred bears then tested on blurred versions of the same objects appearing in each search display. Guidance estimates were based on target probabilities obtained from these detectors. To model recognition, nine bear/nonbear classifiers, trained and tested on unblurred objects, were used to classify the object that would be fixated first (based on the detector estimates) as a teddy bear or a distractor. Patterns of categorical guidance and recognition accuracy were modeled almost perfectly by an HMAX model in combination with a color histogram feature. We conclude that guidance and recognition in the context of search are not separate processes mediated by different features, and that what the literature knows as guidance is really recognition performed on blurred objects viewed in the visual periphery. PMID:24105460

  15. A Survey on Automatic Speaker Recognition Systems

    NASA Astrophysics Data System (ADS)

    Saquib, Zia; Salam, Nirmala; Nair, Rekha P.; Pandey, Nipun; Joshi, Akanksha

    Human listeners are capable of identifying a speaker, over the telephone or an entryway out of sight, by listening to the voice of the speaker. Achieving this intrinsic human specific capability is a major challenge for Voice Biometrics. Like human listeners, voice biometrics uses the features of a person's voice to ascertain the speaker's identity. The best-known commercialized forms of voice Biometrics is Speaker Recognition System (SRS). Speaker recognition is the computing task of validating a user's claimed identity using characteristics extracted from their voices. This literature survey paper gives brief introduction on SRS, and then discusses general architecture of SRS, biometric standards relevant to voice/speech, typical applications of SRS, and current research in Speaker Recognition Systems. We have also surveyed various approaches for SRS.

  16. Automatic stereoscopic system for person recognition

    NASA Astrophysics Data System (ADS)

    Murynin, Alexander B.; Matveev, Ivan A.; Kuznetsov, Victor D.

    1999-06-01

    A biometric access control system based on identification of human face is presented. The system developed performs remote measurements of the necessary face features. Two different scenarios of the system behavior are implemented. The first one assumes the verification of personal data entered by visitor from console using keyboard or card reader. The system functions as an automatic checkpoint, that strictly controls access of different visitors. The other scenario makes it possible to identify visitors without any person identifier or pass. Only person biometrics are used to identify the visitor. The recognition system automatically finds necessary identification information preliminary stored in the database. Two laboratory models of recognition system were developed. The models are designed to use different information types and sources. In addition to stereoscopic images inputted to computer from cameras the models can use voice data and some person physical characteristics such as person's height, measured by imaging system.

  17. Object recognition test for studying cognitive impairments in animal models of Alzheimer's disease.

    PubMed

    Bengoetxea, Xabier; Rodriguez-Perdigon, Manuel; Ramirez, Maria J

    2015-06-01

    Animal models are essential resources in basic research and drug discovery in the field of Alzheimer's disease (AD). As the main clinical feature in AD is cognitive failure, the ultimate readout for any interventions or the ultimate goal in research should be measures of learning and memory. Although there is a wealth of genetic and biochemical studies on proposed AD pathogenic pathways, the aetiology of the illness remains unsolved. Therefore, assessment by cognitive assays should target relevant memory systems without assumptions about pathogenesis. The description of several tests that are available for assessing cognitive functioning in animal models can be found in literature. Among the behavioural test, the novel object recognition (NOR) task is a method to measure a specific form of recognition memory. It is based on the spontaneous behaviour of rodents and offers the advantage of not needing external motivation, reward or punishment. Therefore, the NOR test has been increasingly used as an experimental tool in assessing drug effects on memory and investigating the neural mechanisms underlying learning and memory. This review describes the basic procedure, modifications, practical considerations, and the requirements and caveats of this behavioural paradigm to be considered as appropriate for the study of AD. Altogether, NOR test could be considered as a very useful instrument that allows researchers to explore the cognitive status of rodents, and hence, for studying AD related pathological mechanisms or treatments.

  18. Object recognition test for studying cognitive impairments in animal models of Alzheimer's disease.

    PubMed

    Bengoetxea, Xabier; Rodriguez-Perdigon, Manuel; Ramirez, Maria J

    2015-01-01

    Animal models are essential resources in basic research and drug discovery in the field of Alzheimer's disease (AD). As the main clinical feature in AD is cognitive failure, the ultimate readout for any interventions or the ultimate goal in research should be measures of learning and memory. Although there is a wealth of genetic and biochemical studies on proposed AD pathogenic pathways, the aetiology of the illness remains unsolved. Therefore, assessment by cognitive assays should target relevant memory systems without assumptions about pathogenesis. The description of several tests that are available for assessing cognitive functioning in animal models can be found in literature. Among the behavioural test, the novel object recognition (NOR) task is a method to measure a specific form of recognition memory. It is based on the spontaneous behaviour of rodents and offers the advantage of not needing external motivation, reward or punishment. Therefore, the NOR test has been increasingly used as an experimental tool in assessing drug effects on memory and investigating the neural mechanisms underlying learning and memory. This review describes the basic procedure, modifications, practical considerations, and the requirements and caveats of this behavioural paradigm to be considered as appropriate for the study of AD. Altogether, NOR test could be considered as a very useful instrument that allows researchers to explore the cognitive status of rodents, and hence, for studying AD related pathological mechanisms or treatments. PMID:25961683

  19. Grouping in object recognition: the role of a Gestalt law in letter identification.

    PubMed

    Pelli, Denis G; Majaj, Najib J; Raizman, Noah; Christian, Christopher J; Kim, Edward; Palomares, Melanie C

    2009-02-01

    The Gestalt psychologists reported a set of laws describing how vision groups elements to recognize objects. The Gestalt laws "prescribe for us what we are to recognize 'as one thing'" (Kohler, 1920). Were they right? Does object recognition involve grouping? Tests of the laws of grouping have been favourable, but mostly assessed only detection, not identification, of the compound object. The grouping of elements seen in the detection experiments with lattices and "snakes in the grass" is compelling, but falls far short of the vivid everyday experience of recognizing a familiar, meaningful, named thing, which mediates the ordinary identification of an object. Thus, after nearly a century, there is hardly any evidence that grouping plays a role in ordinary object recognition. To assess grouping in object recognition, we made letters out of grating patches and measured threshold contrast for identifying these letters in visual noise as a function of perturbation of grating orientation, phase, and offset. We define a new measure, "wiggle", to characterize the degree to which these various perturbations violate the Gestalt law of good continuation. We find that efficiency for letter identification is inversely proportional to wiggle and is wholly determined by wiggle, independent of how the wiggle was produced. Thus the effects of three different kinds of shape perturbation on letter identifiability are predicted by a single measure of goodness of continuation. This shows that letter identification obeys the Gestalt law of good continuation and may be the first confirmation of the original Gestalt claim that object recognition involves grouping.

  20. Image enhancement method for fingerprint recognition system.

    PubMed

    Li, Shunshan; Wei, Min; Tang, Haiying; Zhuang, Tiange; Buonocore, Michael

    2005-01-01

    Image enhancement plays an important role in Fingerprint Recognition System. In this paper fingerprint image enhancement method, a refined Gabor filter, is presented. This enhancement method can connect the ridge breaks, ensures the maximal gray values located at the ridge center and has the ability to compensate for the nonlinear deformations. The result shows it can improve the performance of image enhancement.

  1. Spontaneous object recognition: a promising approach to the comparative study of memory

    PubMed Central

    Blaser, Rachel; Heyser, Charles

    2015-01-01

    Spontaneous recognition of a novel object is a popular measure of exploratory behavior, perception and recognition memory in rodent models. Because of its relative simplicity and speed of testing, the variety of stimuli that can be used, and its ecological validity across species, it is also an attractive task for comparative research. To date, variants of this test have been used with vertebrate and invertebrate species, but the methods have seldom been sufficiently standardized to allow cross-species comparison. Here, we review the methods necessary for the study of novel object recognition in mammalian and non-mammalian models, as well as the results of these experiments. Critical to the use of this test is an understanding of the organism’s initial response to a novel object, the modulation of exploration by context, and species differences in object perception and exploratory behaviors. We argue that with appropriate consideration of species differences in perception, object affordances, and natural exploratory behaviors, the spontaneous object recognition test can be a valid and versatile tool for translational research with non-mammalian models. PMID:26217207

  2. Orientation estimation of anatomical structures in medical images for object recognition

    NASA Astrophysics Data System (ADS)

    Bağci, Ulaş; Udupa, Jayaram K.; Chen, Xinjian

    2011-03-01

    Recognition of anatomical structures is an important step in model based medical image segmentation. It provides pose estimation of objects and information about "where" roughly the objects are in the image and distinguishing them from other object-like entities. In,1 we presented a general method of model-based multi-object recognition to assist in segmentation (delineation) tasks. It exploits the pose relationship that can be encoded, via the concept of ball scale (b-scale), between the binary training objects and their associated grey images. The goal was to place the model, in a single shot, close to the right pose (position, orientation, and scale) in a given image so that the model boundaries fall in the close vicinity of object boundaries in the image. Unlike position and scale parameters, we observe that orientation parameters require more attention when estimating the pose of the model as even small differences in orientation parameters can lead to inappropriate recognition. Motivated from the non-Euclidean nature of the pose information, we propose in this paper the use of non-Euclidean metrics to estimate orientation of the anatomical structures for more accurate recognition and segmentation. We statistically analyze and evaluate the following metrics for orientation estimation: Euclidean, Log-Euclidean, Root-Euclidean, Procrustes Size-and-Shape, and mean Hermitian metrics. The results show that mean Hermitian and Cholesky decomposition metrics provide more accurate orientation estimates than other Euclidean and non-Euclidean metrics.

  3. Automated recognition system for power quality disturbances

    NASA Astrophysics Data System (ADS)

    Abdelgalil, Tarek

    The application of deregulation policies in electric power systems has resulted in the necessity to quantify the quality of electric power. This fact highlights the need for a new monitoring strategy which is capable of tracking, detecting, classifying power quality disturbances, and then identifying the source of the disturbance. The objective of this work is to design an efficient and reliable power quality monitoring strategy that uses the advances in signal processing and pattern recognition to overcome the deficiencies that exist in power quality monitoring devices. The purposed monitoring strategy has two stages. The first stage is to detect, track, and classify any power quality violation by the use of on-line measurements. In the second stage, the source of the classified power quality disturbance must be identified. In the first stage, an adaptive linear combiner is used to detect power quality disturbances. Then, the Teager Energy Operator and Hilbert Transform are utilized for power quality event tracking. After the Fourier, Wavelet, and Walsh Transforms are employed for the feature extraction, two approaches are then exploited to classify the different power quality disturbances. The first approach depends on comparing the disturbance to be classified with a stored set of signatures for different power quality disturbances. The comparison is developed by using Hidden Markov Models and Dynamic Time Warping. The second approach depends on employing an inductive inference to generate the classification rules directly from the data. In the second stage of the new monitoring strategy, only the problem of identifying the location of the switched capacitor which initiates the transients is investigated. The Total Least Square-Estimation of Signal Parameters via Rotational Invariance Technique is adopted to estimate the amplitudes and frequencies of the various modes contained in the voltage signal measured at the facility entrance. After extracting the

  4. Humoral pattern recognition and the complement system.

    PubMed

    Degn, S E; Thiel, S

    2013-08-01

    In the context of immunity, pattern recognition is the art of discriminating friend from foe and innocuous from noxious. The basis of discrimination is the existence of evolutionarily conserved patterns on microorganisms, which are intrinsic to these microorganisms and necessary for their function and existence. Such immutable or slowly evolving patterns are ideal handles for recognition and have been targeted by early cellular immune defence mechanisms such as Toll-like receptors, NOD-like receptors, RIG-I-like receptors, C-type lectin receptors and by humoral defence mechanisms such as the complement system. Complement is a proteolytic cascade system comprising around 35 different soluble and membrane-bound proteins. It constitutes a central part of the innate immune system, mediating several major innate effector functions and modulating adaptive immune responses. The complement cascade proceeds via controlled, limited proteolysis and conformational changes of constituent proteins through three activation pathways: the classical pathway, the alternative pathway and the lectin pathway, which converge in common effector functions. Here, we review the nature of the pattern recognition molecules involved in complement activation, as well as their close relatives with no or unknown capacity for activating complement. We proceed to examine the composition of the pattern recognition complexes involved in complement activation, focusing on those of the lectin pathway, and arrive at a new model for their mechanism of operation, supported by recently emerging evidence.

  5. Recognition of partially occluded threat objects using the annealed Hopefield network

    NASA Technical Reports Server (NTRS)

    Kim, Jung H.; Yoon, Sung H.; Park, Eui H.; Ntuen, Celestine A.

    1992-01-01

    Recognition of partially occluded objects has been an important issue to airport security because occlusion causes significant problems in identifying and locating objects during baggage inspection. The neural network approach is suitable for the problems in the sense that the inherent parallelism of neural networks pursues many hypotheses in parallel resulting in high computation rates. Moreover, they provide a greater degree of robustness or fault tolerance than conventional computers. The annealed Hopfield network which is derived from the mean field annealing (MFA) has been developed to find global solutions of a nonlinear system. In the study, it has been proven that the system temperature of MFA is equivalent to the gain of the sigmoid function of a Hopfield network. In our early work, we developed the hybrid Hopfield network (HHN) for fast and reliable matching. However, HHN doesn't guarantee global solutions and yields false matching under heavily occluded conditions because HHN is dependent on initial states by its nature. In this paper, we present the annealed Hopfield network (AHN) for occluded object matching problems. In AHN, the mean field theory is applied to the hybird Hopfield network in order to improve computational complexity of the annealed Hopfield network and provide reliable matching under heavily occluded conditions. AHN is slower than HHN. However, AHN provides near global solutions without initial restrictions and provides less false matching than HHN. In conclusion, a new algorithm based upon a neural network approach was developed to demonstrate the feasibility of the automated inspection of threat objects from x-ray images. The robustness of the algorithm is proved by identifying occluded target objects with large tolerance of their features.

  6. Eyeblink Conditioning and Novel Object Recognition in the Rabbit: Behavioral Paradigms for Assaying Psychiatric Diseases

    PubMed Central

    Weiss, Craig; Disterhoft, John F.

    2015-01-01

    Analysis of data collected from behavioral paradigms has provided important information for understanding the etiology and progression of diseases that involve neural regions mediating abnormal behavior. The trace eyeblink conditioning (EBC) paradigm is particularly suited to examine cerebro-cerebellar interactions since the paradigm requires the cerebellum, forebrain, and awareness of the stimulus contingencies. Impairments in acquiring EBC have been noted in several neuropsychiatric conditions, including schizophrenia, Alzheimer’s disease (AD), progressive supranuclear palsy, and post-traumatic stress disorder. Although several species have been used to examine EBC, the rabbit is unique in its tolerance for restraint, which facilitates imaging, its relatively large skull that facilitates chronic neuronal recordings, a genetic sequence for amyloid that is identical to humans which makes it a valuable model to study AD, and in contrast to rodents, it has a striatum that is differentiated into a caudate and a putamen that facilitates analysis of diseases involving the striatum. This review focuses on EBC during schizophrenia and AD since impairments in cerebro-cerebellar connections have been hypothesized to lead to a cognitive dysmetria. We also relate EBC to conditioned avoidance responses that are more often examined for effects of antipsychotic medications, and we propose that an analysis of novel object recognition (NOR) may add to our understanding of how the underlying neural circuitry has changed during disease states. We propose that the EBC and NOR paradigms will help to determine which therapeutics are effective for treating the cognitive aspects of schizophrenia and AD, and that neuroimaging may reveal biomarkers of the diseases and help to evaluate potential therapeutics. The rabbit, thus, provides an important translational system for studying neural mechanisms mediating maladaptive behaviors that underlie some psychiatric diseases, especially

  7. Coincident orientation of objects and viewpoint-dependence in scene recognition.

    PubMed

    Li, Jing; Zhang, Kan

    2012-02-01

    Viewpoint-dependence is a well-known phenomenon in which participants' spatial memory is better for previously experienced points of view than for novel ones. In the current study, partial-scene-recognition was used to examine the effect of coincident orientation of all the objects on viewpoint-dependence in spatial memory. When objects in scenes had no clear orientations (e.g., balls), participants' recognition of experienced directions was better than that of novel ones, indicating that there was viewpoint-dependence. However, when the objects in scenes were toy bears with clear orientations, the coincident orientation of objects (315 degrees), which was not experienced, shared the advantage of the experienced direction (0 degrees), and participants were equally likely to choose either direction when reconstructing the spatial representation in memory. These findings suggest that coincident orientation of objects may affect egocentric representations in spatial memory. PMID:22582697

  8. Environmental enrichment improves novel object recognition and enhances agonistic behavior in male mice.

    PubMed

    Mesa-Gresa, Patricia; Pérez-Martinez, Asunción; Redolat, Rosa

    2013-01-01

    Environmental enrichment (EE) is an experimental paradigm in which rodents are housed in complex environments containing objects that provide stimulation, the effects of which are expected to improve the welfare of these subjects. EE has been shown to considerably improve learning and memory in rodents. However, knowledge about the effects of EE on social interaction is generally limited and rather controversial. Thus, our aim was to evaluate both novel object recognition and agonistic behavior in NMRI mice receiving EE, hypothesizing enhanced cognition and slightly enhanced agonistic interaction upon EE rearing. During a 4-week period half the mice (n = 16) were exposed to EE and the other half (n = 16) remained in a standard environment (SE). On PND 56-57, animals performed the object recognition test, in which recognition memory was measured using a discrimination index. The social interaction test consisted of an encounter between an experimental animal and a standard opponent. Results indicated that EE mice explored the new object for longer periods than SE animals (P < .05). During social encounters, EE mice devoted more time to sociability and agonistic behavior (P < .05) than their non-EE counterparts. In conclusion, EE has been shown to improve object recognition and increase agonistic behavior in adolescent/early adulthood mice. In the future we intend to extend this study on a longitudinal basis in order to assess in more depth the effect of EE and the consistency of the above-mentioned observations in NMRI mice.

  9. Securing iris recognition systems against masquerade attacks

    NASA Astrophysics Data System (ADS)

    Galbally, Javier; Gomez-Barrero, Marta; Ross, Arun; Fierrez, Julian; Ortega-Garcia, Javier

    2013-05-01

    A novel two-stage protection scheme for automatic iris recognition systems against masquerade attacks carried out with synthetically reconstructed iris images is presented. The method uses different characteristics of real iris images to differentiate them from the synthetic ones, thereby addressing important security flaws detected in state-of-the-art commercial systems. Experiments are carried out on the publicly available Biosecure Database and demonstrate the efficacy of the proposed security enhancing approach.

  10. Augmented reality three-dimensional object visualization and recognition with axially distributed sensing.

    PubMed

    Markman, Adam; Shen, Xin; Hua, Hong; Javidi, Bahram

    2016-01-15

    An augmented reality (AR) smartglass display combines real-world scenes with digital information enabling the rapid growth of AR-based applications. We present an augmented reality-based approach for three-dimensional (3D) optical visualization and object recognition using axially distributed sensing (ADS). For object recognition, the 3D scene is reconstructed, and feature extraction is performed by calculating the histogram of oriented gradients (HOG) of a sliding window. A support vector machine (SVM) is then used for classification. Once an object has been identified, the 3D reconstructed scene with the detected object is optically displayed in the smartglasses allowing the user to see the object, remove partial occlusions of the object, and provide critical information about the object such as 3D coordinates, which are not possible with conventional AR devices. To the best of our knowledge, this is the first report on combining axially distributed sensing with 3D object visualization and recognition for applications to augmented reality. The proposed approach can have benefits for many applications, including medical, military, transportation, and manufacturing.

  11. A Genetic-Algorithm-Based Explicit Description of Object Contour and its Ability to Facilitate Recognition.

    PubMed

    Wei, Hui; Tang, Xue-Song

    2015-11-01

    Shape representation is an extremely important and longstanding problem in the field of pattern recognition. Closed contour, which refers to shape contour, plays a crucial role in the comparison of shapes. Because shape contour is the most stable, distinguishable, and invariable feature of an object, it is useful to incorporate it into the recognition process. This paper proposes a method based on genetic algorithms. The proposed method can be used to identify the most common contour fragments, which can be used to represent the contours of a shape category. The common fragments clarify the particular logics included in the contours. This paper shows that the explicit representation of the shape contour contributes significantly to shape representation and object recognition.

  12. AVNG system objectives and concept

    SciTech Connect

    Macarthur, Duncan W; Thron, Jonathan; Razinkov, Sergey; Livke, Alexander; Kondratov, Sergey

    2010-01-01

    Any verification measurement performed on potentially classified nuclear material must satisfy two constraints. First and foremost, no classified information can be released to the monitoring party. At the same time, the monitoring party must gain sufficient confidence from the measurement to believe that the material being measured is consistent with the host's declarations concerning that material. The attribute measurement technique addresses both concerns by measuring several attributes of the nuclear material and displaying unclassified results through green (indicating that the material does possess the specified attribute) and red (indicating that the material does not possess the specified attribute) lights. The AVNG that we describe is an attribute measurement system built by RFNC-VNIIEF in Sarov, Russia. The AVNG measures the three attributes of 'plutonium presence,' 'plutonium mass >2 kg,' and 'plutonium isotopic ratio ({sup 240}Pu to {sup 239}Pu) <0.1' and was demonstrated in Sarov for a joint US/Russian audience in June 2009. In this presentation, we will outline the goals and objectives of the AVNG measurement system. These goals are driven by the two, sometimes conflicting, requirements mentioned above. We will describe the conceptual design of the AVNG and show how this conceptual design grew out of these goals and objectives.

  13. Feature discovery in gray level imagery for one-class object recognition

    SciTech Connect

    Koch, M.W.; Moya, M.M.

    1993-12-31

    Feature extraction transforms an object`s image representation to an alternate reduced representation. In one-class object recognition, we would like this alternate representation to give improved discrimination between the object and all possible non-objects and improved generation between different object poses. Feature selection can be time-consuming and difficult to optimize so we have investigated unsupervised neural networks for feature discovery. We first discuss an inherent limitation in competitive type neural networks for discovering features in gray level images. We then show how Sanger`s Generalized Hebbian Algorithm (GHA) removes this limitation and describe a novel GHA application for learning object features that discriminate the object from clutter. Using a specific example, we show how these features are better at distinguishing the target object from other nontarget object with Carpenter`s ART 2-A as the pattern classifier.

  14. Phoneme fuzzy characterization in speech recognition systems

    NASA Astrophysics Data System (ADS)

    Beritelli, Francesco; Borrometi, Luca; Cuce, Antonino

    1997-10-01

    The acoustic approach to speech recognition has an important advantage compared with pattern recognition approach: it presents a lower complexity because it doesn't require explicit structures such as the hidden Markov model. In this work, we show how to characterize some phonetic classes of the Italian language in order to obtain a speaker and vocabulary independent speech recognition system. A phonetic data base is carried out with 200 continuous speech sentences of 12 speakers, 6 females and 6 males. The sentences are sampled at 8000 Hz and manual labelled with Asystem Sound Impression Software to obtain about 1600 units. We analyzed several speech parameters such as formants, LPC and reflection coefficients, energy, normal/differential zero crossing rate, cepstral and autocorrelation coefficients. The aim is the achievement of a phonetic recognizer to facilitate the so- called lexical access problem, that is to decode phonetic units into complete sense word strings. The knowledge is supplied to the recognizer in terms of fuzzy systems. The utilized software is called adaptive fuzzy modeler and it belongs to the rule generator family. A procedure has been implemented to integrate in the fuzzy system an 'expert' knowledge in order to obtain significant improvements in the recognition accuracy. Up to this point the tests show a recognition rate of 92% for the vocal class, 89% for the fricatives class and 94% for the nasal class, utilizing 1000 phonemes in phase of learning and 600 phonemes in phase of testing. Our intention is to complete the fuzzy recognizer extending this work to the other phonetic classes.

  15. HONTIOR - HIGHER-ORDER NEURAL NETWORK FOR TRANSFORMATION INVARIANT OBJECT RECOGNITION

    NASA Technical Reports Server (NTRS)

    Spirkovska, L.

    1994-01-01

    Neural networks have been applied in numerous fields, including transformation invariant object recognition, wherein an object is recognized despite changes in the object's position in the input field, size, or rotation. One of the more successful neural network methods used in invariant object recognition is the higher-order neural network (HONN) method. With a HONN, known relationships are exploited and the desired invariances are built directly into the architecture of the network, eliminating the need for the network to learn invariance to transformations. This results in a significant reduction in the training time required, since the network needs to be trained on only one view of each object, not on numerous transformed views. Moreover, one hundred percent accuracy is guaranteed for images characterized by the built-in distortions, providing noise is not introduced through pixelation. The program HONTIOR implements a third-order neural network having invariance to translation, scale, and in-plane rotation built directly into the architecture, Thus, for 2-D transformation invariance, the network needs only to be trained on just one view of each object. HONTIOR can also be used for 3-D transformation invariant object recognition by training the network only on a set of out-of-plane rotated views. Historically, the major drawback of HONNs has been that the size of the input field was limited to the memory required for the large number of interconnections in a fully connected network. HONTIOR solves this problem by coarse coding the input images (coding an image as a set of overlapping but offset coarser images). Using this scheme, large input fields (4096 x 4096 pixels) can easily be represented using very little virtual memory (30Mb). The HONTIOR distribution consists of three main programs. The first program contains the training and testing routines for a third-order neural network. The second program contains the same training and testing procedures as the

  16. Effects of exposure to heavy particles and aging on object recognition memory in rats

    NASA Astrophysics Data System (ADS)

    Rabin, Bernard; Joseph, James; Shukitt-Hale, Barbara; Carrihill-Knoll, Kirsty; Shannahan, Ryan; Hering, Kathleen

    Exposure to HZE particles produces changes in neurocognitive performance. These changes, including deficits in spatial learning and memory, object recognition memory and operant responding, are also observed in the aged organism. As such, it has been proposed that exposure to heavy particles produces "accelerated aging". Because aging is an ongoing process, it is possible that there would be an interaction between the effects of exposure and the effects of aging, such that doses of HZE particles that do not affect the performance of younger organisms will affect the performance of organisms as they age. The present experiments were designed to test the hypothesis that young rats that had been exposed to HZE particles would show a progressive deterioration in object recognition memory as a function of the age of testing. Rats were exposed to 12 C, 28 S or 48 Ti particles at the N.A.S.A. Space Radiation Laboratory at Brookhaven National Laboratory. Following irradiation the rats were shipped to UMBC for behavioral testing. HZE particle-induced changes in object recognition memory were tested using a standard procedure: rats were placed in an open field and allowed to interact with two identical objects for up to 30 sec; twenty-four hrs later the rats were again placed in the open field, this time containing one familiar and one novel object. Non-irradiated control animals spent significantly more time with the novel object than with the familiar object. In contrast, the rats that been exposed to heavy particles spent equal amounts of time with both the novel and familiar object. The lowest dose of HZE particles which produced a disruption of object recognition memory was determined three months and eleven months following exposure. The threshold dose needed to disrupt object recognition memory three months following irradiation varied as a function of the specific particle and energy. When tested eleven months following irradiation, doses of HZE particles that did

  17. Recognition of 3D objects for autonomous mobile robot's navigation in automated shipbuilding

    NASA Astrophysics Data System (ADS)

    Lee, Hyunki; Cho, Hyungsuck

    2007-10-01

    Nowadays many parts of shipbuilding process are automated, but the painting process is not, because of the difficulty of automated on-line painting quality measurement, harsh painting environment and the difficulty of robot navigation. However, the painting automation is necessary, because it can provide consistent performance of painting film thickness. Furthermore, autonomous mobile robots are strongly required for flexible painting work. However, the main problem of autonomous mobile robot's navigation is that there are many obstacles which are not expressed in the CAD data. To overcome this problem, obstacle detection and recognition are necessary to avoid obstacles and painting work effectively. Until now many object recognition algorithms have been studied, especially 2D object recognition methods using intensity image have been widely studied. However, in our case environmental illumination does not exist, so these methods cannot be used. To overcome this, to use 3D range data must be used, but the problem of using 3D range data is high computational cost and long estimation time of recognition due to huge data base. In this paper, we propose a 3D object recognition algorithm based on PCA (Principle Component Analysis) and NN (Neural Network). In the algorithm, the novelty is that the measured 3D range data is transformed into intensity information, and then adopts the PCA and NN algorithm for transformed intensity information to reduce the processing time and make the data easy to handle which are disadvantages of previous researches of 3D object recognition. A set of experimental results are shown to verify the effectiveness of the proposed algorithm.

  18. Intelligent recognitive systems in nanomedicine.

    PubMed

    Culver, Heidi; Daily, Adam; Khademhosseini, Ali; Peppas, Nicholas

    2014-05-01

    There is a bright future in the development and utilization of nanoscale systems based on intelligent materials that can respond to external input providing a beneficial function. Specific functional groups can be incorporated into polymers to make them responsive to environmental stimuli such as pH, temperature, or varying concentrations of biomolecules. The fusion of such "intelligent" biomaterials with nanotechnology has led to the development of powerful therapeutic and diagnostic platforms. For example, targeted release of proteins and chemotherapeutic drugs has been achieved using pH-responsive nanocarriers while biosensors with ultra-trace detection limits are being made using nanoscale, molecularly imprinted polymers. The efficacy of therapeutics and the sensitivity of diagnostic platforms will continue to progress as unique combinations of responsive polymers and nanomaterials emerge.

  19. Intelligent recognitive systems in nanomedicine

    PubMed Central

    Culver, Heidi; Daily, Adam; Khademhosseini, Ali

    2014-01-01

    There is a bright future in the development and utilization of nanoscale systems based on intelligent materials that can respond to external input providing a beneficial function. Specific functional groups can be incorporated into polymers to make them responsive to environmental stimuli such as pH, temperature, or varying concentrations of biomolecules. The fusion of such “intelligent” biomaterials with nanotechnology has led to the development of powerful therapeutic and diagnostic platforms. For example, targeted release of proteins and chemotherapeutic drugs has been achieved using pH-responsive nanocarriers while biosensors with ultra-trace detection limits are being made using nanoscale, molecularly imprinted polymers. The efficacy of therapeutics and the sensitivity of diagnostic platforms will continue to progress as unique combinations of responsive polymers and nanomaterials emerge. PMID:24860724

  20. Physical exercise during pregnancy improves object recognition memory in adult offspring.

    PubMed

    Robinson, A M; Bucci, D J

    2014-01-01

    Exercising during pregnancy has been shown to improve spatial learning and short-term memory, as well as increase brain-derived neurotrophic factor mRNA levels and hippocampal cell survival in juvenile offspring. However, it remains unknown if these effects endure into adulthood. In addition, few studies have considered how maternal exercise can impact cognitive functions that do not rely on the hippocampus. To address these issues, the present study tested the effects of maternal exercise during pregnancy on object recognition memory, which relies on the perirhinal cortex (PER), in adult offspring. Pregnant rats were given access to a running wheel throughout gestation and the adult male offspring were subsequently tested in an object recognition memory task at three different time points, each spaced 2-weeks apart, beginning at 60 days of age. At each time point, offspring from exercising mothers were able to successfully discriminate between novel and familiar objects in that they spent more time exploring the novel object than the familiar object. The offspring of non-exercising mothers were not able to successfully discriminate between objects and spent an equal amount of time with both objects. A subset of rats was euthanized 1h after the final object recognition test to assess c-FOS expression in the PER. The offspring of exercising mothers had more c-FOS expression in the PER than the offspring of non-exercising mothers. By comparison, c-FOS levels in the adjacent auditory cortex did not differ between groups. These results indicate that maternal exercise during pregnancy can improve object recognition memory in adult male offspring and increase c-FOS expression in the PER; suggesting that exercise during the gestational period may enhance brain function of the offspring. PMID:24157927

  1. Automatic TLI recognition system, user`s guide

    SciTech Connect

    Lassahn, G.D.

    1997-02-01

    This report describes how to use an automatic target recognition system (version 14). In separate volumes are a general description of the ATR system, Automatic TLI Recognition System, General Description, and a programmer`s manual, Automatic TLI Recognition System, Programmer`s Guide.

  2. Developmental Trajectories of Part-Based and Configural Object Recognition in Adolescence

    ERIC Educational Resources Information Center

    Juttner, Martin; Wakui, Elley; Petters, Dean; Kaur, Surinder; Davidoff, Jules

    2013-01-01

    Three experiments assessed the development of children's part and configural (part-relational) processing in object recognition during adolescence. In total, 312 school children aged 7-16 years and 80 adults were tested in 3-alternative forced choice (3-AFC) tasks. They judged the correct appearance of upright and inverted presented familiar…

  3. Mechanisms and Neural Basis of Object and Pattern Recognition: A Study with Chess Experts

    ERIC Educational Resources Information Center

    Bilalic, Merim; Langner, Robert; Erb, Michael; Grodd, Wolfgang

    2010-01-01

    Comparing experts with novices offers unique insights into the functioning of cognition, based on the maximization of individual differences. Here we used this expertise approach to disentangle the mechanisms and neural basis behind two processes that contribute to everyday expertise: object and pattern recognition. We compared chess experts and…

  4. Developmental Changes in Visual Object Recognition between 18 and 24 Months of Age

    ERIC Educational Resources Information Center

    Pereira, Alfredo F.; Smith, Linda B.

    2009-01-01

    Two experiments examined developmental changes in children's visual recognition of common objects during the period of 18 to 24 months. Experiment 1 examined children's ability to recognize common category instances that presented three different kinds of information: (1) richly detailed and prototypical instances that presented both local and…

  5. A neural-network appearance-based 3-D object recognition using independent component analysis.

    PubMed

    Sahambi, H S; Khorasani, K

    2003-01-01

    This paper presents results on appearance-based three-dimensional (3-D) object recognition (3DOR) accomplished by utilizing a neural-network architecture developed based on independent component analysis (ICA). ICA has already been applied for face recognition in the literature with encouraging results. In this paper, we are exploring the possibility of utilizing the redundant information in the visual data to enhance the view based object recognition. The underlying premise here is that since ICA uses high-order statistics, it should in principle outperform principle component analysis (PCA), which does not utilize statistics higher than two, in the recognition task. Two databases of images captured by a CCD camera are used. It is demonstrated that ICA did perform better than PCA in one of the databases, but interestingly its performance was no better than PCA in the case of the second database. Thus, suggesting that the use of ICA may not necessarily always give better results than PCA, and that the application of ICA is highly data dependent. Various factors affecting the differences in the recognition performance using both methods are also discussed. PMID:18237997

  6. Blockade of Glutamatergic Transmission in Perirhinal Cortex Impairs Object Recognition Memory in Macaques

    PubMed Central

    Forcelli, Patrick A.; Wellman, Laurie L.; Dybdal, David; Dubach, Mark F.; Gale, Karen

    2015-01-01

    The perirhinal cortex (PRc) is essential for visual recognition memory, as shown by electrophysiological recordings and lesion studies in a variety of species. However, relatively little is known about the functional contributions of perirhinal subregions. Here we used a systematic mapping approach to identify the critical subregions of PRc through transient, focal blockade of glutamate receptors by intracerebral infusion of kynurenic acid. Nine macaques were tested for visual recognition memory using the delayed nonmatch-to-sample task. We found that inactivation of medial PRc (consisting of Area 35 together with the medial portion of Area 36), but not lateral PRc (the lateral portion of Area 36), resulted in a significant delay-dependent impairment. Significant impairment was observed with 30 and 60 s delays but not with 10 s delays. The magnitude of impairment fell within the range previously reported after PRc lesions. Furthermore, we identified a restricted area located within the most anterior part of medial PRc as critical for this effect. Moreover, we found that focal blockade of either NMDA receptors by the receptor-specific antagonist AP-7 or AMPA receptors by the receptor-specific antagonist NBQX was sufficient to disrupt object recognition memory. The present study expands the knowledge of the role of PRc in recognition memory by identifying a subregion within this area that is critical for this function. Our results also indicate that, like in the rodent, both NMDA and AMPA-mediated transmission contributes to object recognition memory. PMID:25810533

  7. Progestogens’ effects and mechanisms for object recognition memory across the lifespan

    PubMed Central

    Walf, Alicia A.; Koonce, Carolyn J.; Frye, Cheryl A.

    2016-01-01

    This review explores the effects of female reproductive hormones, estrogens and progestogens, with a focus on progesterone and allopregnanolone, on object memory. Progesterone and its metabolites, in particular allopregnanolone, exert various effects on both cognitive and non-mnemonic functions in females. The well-known object recognition task is a valuable experimental paradigm that can be used to determine the effects and mechanisms of progestogens for mnemonic effects across the lifespan, which will be discussed herein. In this task there is little test-decay when different objects are used as targets and baseline valance for objects is controlled. This allows repeated testing, within-subjects designs, and longitudinal assessments, which aid understanding of changes in hormonal milieu. Objects are not aversive or food-based, which are hormone-sensitive factors. This review focuses on published data from our laboratory, and others, using the object recognition task in rodents to assess the role and mechanisms of progestogens throughout the lifespan. Improvements in object recognition performance of rodents are often associated with higher hormone levels in the hippocampus and prefrontal cortex during natural cycles, with hormone replacement following ovariectomy in young animals, or with aging. The capacity for reversal of age- and reproductive senescence-related decline in cognitive performance, and changes in neural plasticity that may be dissociated from peripheral effects with such decline, are discussed. The focus here will be on the effects of brain-derived factors, such as the neurosteroid, allopregnanolone, and other hormones, for enhancing object recognition across the lifespan. PMID:26235328

  8. LASSBio-579, a prototype antipsychotic drug, and clozapine are effective in novel object recognition task, a recognition memory model.

    PubMed

    Antonio, Camila B; Betti, Andresa H; Herzfeldt, Vivian; Barreiro, Eliezer J; Fraga, Carlos A M; Rates, Stela M K

    2016-06-01

    Previous studies on the N-phenylpiperazine derivative LASSBio-579 have suggested that LASSBio-579 has an atypical antipsychotic profile. It binds to D2, D4 and 5-HT1A receptors and is effective in animal models of schizophrenia symptoms (prepulse inhibition disruption, apomorphine-induced climbing and amphetamine-induced stereotypy). In the current study, we evaluated the effect of LASSBio-579, clozapine (atypical antipsychotic) and haloperidol (typical antipsychotic) in the novel object recognition task, a recognition memory model with translational value. Haloperidol (0.01 mg/kg, orally) impaired the ability of the animals (CF1 mice) to recognize the novel object on short-term and long-term memory tasks, whereas LASSBio-579 (5 mg/kg, orally) and clozapine (1 mg/kg, orally) did not. In another set of experiments, animals previously treated with ketamine (10 mg/kg, intraperitoneally) or vehicle (saline 1 ml/100 g, intraperitoneally) received LASSBio-579, clozapine or haloperidol at different time-points: 1 h before training (encoding/consolidation); immediately after training (consolidation); or 1 h before long-term memory testing (retrieval). LASSBio-579 and clozapine protected against the long-term memory impairment induced by ketamine when administered at the stages of encoding, consolidation and retrieval of memory. These findings point to the potential of LASSBio-579 for treating cognitive symptoms of schizophrenia and other disorders.

  9. BDNF Expression in Perirhinal Cortex is Associated with Exercise-Induced Improvement in Object Recognition Memory

    PubMed Central

    Hopkins, Michael E.; Bucci, David J.

    2010-01-01

    Physical exercise induces widespread neurobiological adaptations and improves learning and memory. Most research in this field has focused on hippocampus-based spatial tasks and changes in brain-derived neurotrophic factor (BDNF) as a putative substrate underlying exercise-induced cognitive improvements. Chronic exercise can also be anxiolytic and causes adaptive changes in stress reactivity. The present study employed a perirhinal cortex-dependent object recognition task as well as the elevated plus maze to directly test for interactions between the cognitive and anxiolytic effects of exercise in male Long Evans rats. Hippocampal and perirhinal cortex tissue was collected to determine whether the relationship between BDNF and cognitive performance extends to this non-spatial and non-hippocampal-dependent task. We also examined whether the cognitive improvements persisted once the exercise regimen was terminated. Our data indicate that 4 weeks of voluntary exercise every-other-day improved object recognition memory. Importantly, BDNF expression in the perirhinal cortex of exercising rats was strongly correlated with object recognition memory. Exercise also decreased anxiety-like behavior, however there was no evidence to support a relationship between anxiety-like behavior and performance on the novel object recognition task. There was a trend for a negative relationship between anxiety-like behavior and hippocampal BDNF. Neither the cognitive improvements nor the relationship between cognitive function and perirhinal BDNF levels persisted after 2 weeks of inactivity. These are the first data demonstrating that region-specific changes in BDNF protein levels are correlated with exercise-induced improvements in non-spatial memory, mediated by structures outside the hippocampus and are consistent with the theory that, with regard to object recognition, the anxiolytic and cognitive effects of exercise may be mediated through separable mechanisms. PMID:20601027

  10. BDNF expression in perirhinal cortex is associated with exercise-induced improvement in object recognition memory.

    PubMed

    Hopkins, Michael E; Bucci, David J

    2010-09-01

    Physical exercise induces widespread neurobiological adaptations and improves learning and memory. Most research in this field has focused on hippocampus-based spatial tasks and changes in brain-derived neurotrophic factor (BDNF) as a putative substrate underlying exercise-induced cognitive improvements. Chronic exercise can also be anxiolytic and causes adaptive changes in stress-reactivity. The present study employed a perirhinal cortex-dependent object recognition task as well as the elevated plus maze to directly test for interactions between the cognitive and anxiolytic effects of exercise in male Long Evans rats. Hippocampal and perirhinal cortex tissue was collected to determine whether the relationship between BDNF and cognitive performance extends to this non-spatial and non-hippocampal-dependent task. We also examined whether the cognitive improvements persisted once the exercise regimen was terminated. Our data indicate that 4weeks of voluntary exercise every-other-day improved object recognition memory. Importantly, BDNF expression in the perirhinal cortex of exercising rats was strongly correlated with object recognition memory. Exercise also decreased anxiety-like behavior, however there was no evidence to support a relationship between anxiety-like behavior and performance on the novel object recognition task. There was a trend for a negative relationship between anxiety-like behavior and hippocampal BDNF. Neither the cognitive improvements nor the relationship between cognitive function and perirhinal BDNF levels persisted after 2weeks of inactivity. These are the first data demonstrating that region-specific changes in BDNF protein levels are correlated with exercise-induced improvements in non-spatial memory, mediated by structures outside the hippocampus and are consistent with the theory that, with regard to object recognition, the anxiolytic and cognitive effects of exercise may be mediated through separable mechanisms.

  11. Deep Networks Can Resemble Human Feed-forward Vision in Invariant Object Recognition.

    PubMed

    Kheradpisheh, Saeed Reza; Ghodrati, Masoud; Ganjtabesh, Mohammad; Masquelier, Timothée

    2016-01-01

    Deep convolutional neural networks (DCNNs) have attracted much attention recently, and have shown to be able to recognize thousands of object categories in natural image databases. Their architecture is somewhat similar to that of the human visual system: both use restricted receptive fields, and a hierarchy of layers which progressively extract more and more abstracted features. Yet it is unknown whether DCNNs match human performance at the task of view-invariant object recognition, whether they make similar errors and use similar representations for this task, and whether the answers depend on the magnitude of the viewpoint variations. To investigate these issues, we benchmarked eight state-of-the-art DCNNs, the HMAX model, and a baseline shallow model and compared their results to those of humans with backward masking. Unlike in all previous DCNN studies, we carefully controlled the magnitude of the viewpoint variations to demonstrate that shallow nets can outperform deep nets and humans when variations are weak. When facing larger variations, however, more layers were needed to match human performance and error distributions, and to have representations that are consistent with human behavior. A very deep net with 18 layers even outperformed humans at the highest variation level, using the most human-like representations.

  12. Deep Networks Can Resemble Human Feed-forward Vision in Invariant Object Recognition

    NASA Astrophysics Data System (ADS)

    Kheradpisheh, Saeed Reza; Ghodrati, Masoud; Ganjtabesh, Mohammad; Masquelier, Timothée

    2016-09-01

    Deep convolutional neural networks (DCNNs) have attracted much attention recently, and have shown to be able to recognize thousands of object categories in natural image databases. Their architecture is somewhat similar to that of the human visual system: both use restricted receptive fields, and a hierarchy of layers which progressively extract more and more abstracted features. Yet it is unknown whether DCNNs match human performance at the task of view-invariant object recognition, whether they make similar errors and use similar representations for this task, and whether the answers depend on the magnitude of the viewpoint variations. To investigate these issues, we benchmarked eight state-of-the-art DCNNs, the HMAX model, and a baseline shallow model and compared their results to those of humans with backward masking. Unlike in all previous DCNN studies, we carefully controlled the magnitude of the viewpoint variations to demonstrate that shallow nets can outperform deep nets and humans when variations are weak. When facing larger variations, however, more layers were needed to match human performance and error distributions, and to have representations that are consistent with human behavior. A very deep net with 18 layers even outperformed humans at the highest variation level, using the most human-like representations.

  13. Deep Networks Can Resemble Human Feed-forward Vision in Invariant Object Recognition

    PubMed Central

    Kheradpisheh, Saeed Reza; Ghodrati, Masoud; Ganjtabesh, Mohammad; Masquelier, Timothée

    2016-01-01

    Deep convolutional neural networks (DCNNs) have attracted much attention recently, and have shown to be able to recognize thousands of object categories in natural image databases. Their architecture is somewhat similar to that of the human visual system: both use restricted receptive fields, and a hierarchy of layers which progressively extract more and more abstracted features. Yet it is unknown whether DCNNs match human performance at the task of view-invariant object recognition, whether they make similar errors and use similar representations for this task, and whether the answers depend on the magnitude of the viewpoint variations. To investigate these issues, we benchmarked eight state-of-the-art DCNNs, the HMAX model, and a baseline shallow model and compared their results to those of humans with backward masking. Unlike in all previous DCNN studies, we carefully controlled the magnitude of the viewpoint variations to demonstrate that shallow nets can outperform deep nets and humans when variations are weak. When facing larger variations, however, more layers were needed to match human performance and error distributions, and to have representations that are consistent with human behavior. A very deep net with 18 layers even outperformed humans at the highest variation level, using the most human-like representations. PMID:27601096

  14. Deep Networks Can Resemble Human Feed-forward Vision in Invariant Object Recognition.

    PubMed

    Kheradpisheh, Saeed Reza; Ghodrati, Masoud; Ganjtabesh, Mohammad; Masquelier, Timothée

    2016-01-01

    Deep convolutional neural networks (DCNNs) have attracted much attention recently, and have shown to be able to recognize thousands of object categories in natural image databases. Their architecture is somewhat similar to that of the human visual system: both use restricted receptive fields, and a hierarchy of layers which progressively extract more and more abstracted features. Yet it is unknown whether DCNNs match human performance at the task of view-invariant object recognition, whether they make similar errors and use similar representations for this task, and whether the answers depend on the magnitude of the viewpoint variations. To investigate these issues, we benchmarked eight state-of-the-art DCNNs, the HMAX model, and a baseline shallow model and compared their results to those of humans with backward masking. Unlike in all previous DCNN studies, we carefully controlled the magnitude of the viewpoint variations to demonstrate that shallow nets can outperform deep nets and humans when variations are weak. When facing larger variations, however, more layers were needed to match human performance and error distributions, and to have representations that are consistent with human behavior. A very deep net with 18 layers even outperformed humans at the highest variation level, using the most human-like representations. PMID:27601096

  15. Disentangling the contributions of grasp and action representations in the recognition of manipulable objects.

    PubMed

    McNair, Nicolas A; Harris, Irina M

    2012-07-01

    There is an increasing evidence that the action properties of manipulable objects can play a role in object recognition, as objects with similar action properties can facilitate each other's recognition [Helbig et al. Exp Brain Res 174:221-228, 2006]. However, it is unclear whether this modulation is driven by the actions involved in using the object or the grasps afforded by the objects, because these factors have been confounded in previous studies. Here, we attempted to disentangle the relative contributions of the action and grasp properties by using a priming paradigm in which action and grasp similarity between two objects were varied orthogonally. We found that target tools with similar grasp properties to the prime tool were named more accurately than those with dissimilar grasps. However, naming accuracy was not affected by the similarity of action properties between the prime and target tools. This suggests that knowledge about how an object is used is not automatically accessed when identifying a manipulable object. What are automatically accessed are the transformations necessary to interact directly with the object--i.e., the manner in which one grasps the object.

  16. Neural network techniques for invariant recognition and motion tracking of 3-D objects

    SciTech Connect

    Hwang, J.N.; Tseng, Y.H.

    1995-12-31

    Invariant recognition and motion tracking of 3-D objects under partial object viewing are difficult tasks. In this paper, we introduce a new neural network solution that is robust to noise corruption and partial viewing of objects. This method directly utilizes the acquired range data and requires no feature extraction. In the proposed approach, the object is first parametrically represented by a continuous distance transformation neural network (CDTNN) which is trained by the surface points of the exemplar object. When later presented with the surface points of an unknown object, this parametric representation allows the mismatch information to back-propagate through the CDTNN to gradually determine the best similarity transformation (translation and rotation) of the unknown object. The mismatch can be directly measured in the reconstructed representation domain between the model and the unknown object.

  17. Beyond perceptual expertise: revisiting the neural substrates of expert object recognition

    PubMed Central

    Harel, Assaf; Kravitz, Dwight; Baker, Chris I.

    2013-01-01

    Real-world expertise provides a valuable opportunity to understand how experience shapes human behavior and neural function. In the visual domain, the study of expert object recognition, such as in car enthusiasts or bird watchers, has produced a large, growing, and often-controversial literature. Here, we synthesize this literature, focusing primarily on results from functional brain imaging, and propose an interactive framework that incorporates the impact of high-level factors, such as attention and conceptual knowledge, in supporting expertise. This framework contrasts with the perceptual view of object expertise that has concentrated largely on stimulus-driven processing in visual cortex. One prominent version of this perceptual account has almost exclusively focused on the relation of expertise to face processing and, in terms of the neural substrates, has centered on face-selective cortical regions such as the Fusiform Face Area (FFA). We discuss the limitations of this face-centric approach as well as the more general perceptual view, and highlight that expert related activity is: (i) found throughout visual cortex, not just FFA, with a strong relationship between neural response and behavioral expertise even in the earliest stages of visual processing, (ii) found outside visual cortex in areas such as parietal and prefrontal cortices, and (iii) modulated by the attentional engagement of the observer suggesting that it is neither automatic nor driven solely by stimulus properties. These findings strongly support a framework in which object expertise emerges from extensive interactions within and between the visual system and other cognitive systems, resulting in widespread, distributed patterns of expertise-related activity across the entire cortex. PMID:24409134

  18. Implementation of a Peltier-based cooling device for localized deep cortical deactivation during in vivo object recognition testing

    NASA Astrophysics Data System (ADS)

    Marra, Kyle; Graham, Brett; Carouso, Samantha; Cox, David

    2012-02-01

    While the application of local cortical cooling has recently become a focus of neurological research, extended localized deactivation deep within brain structures is still unexplored. Using a wirelessly controlled thermoelectric (Peltier) device and water-based heat sink, we have achieved inactivating temperatures (<20 C) at greater depths (>8 mm) than previously reported. After implanting the device into Long Evans rats' basolateral amygdala (BLA), an inhibitory brain center that controls anxiety and fear, we ran an open field test during which anxiety-driven behavioral tendencies were observed to decrease during cooling, thus confirming the device's effect on behavior. Our device will next be implanted in the rats' temporal association cortex (TeA) and recordings from our signal-tracing multichannel microelectrodes will measure and compare activated and deactivated neuronal activity so as to isolate and study the TeA signals responsible for object recognition. Having already achieved a top performing computational face-recognition system, the lab will utilize this TeA activity data to generalize its computational efforts of face recognition to achieve general object recognition.

  19. Relating visual to verbal semantic knowledge: the evaluation of object recognition in prosopagnosia.

    PubMed

    Barton, Jason J S; Hanif, Hashim; Ashraf, Sohi

    2009-12-01

    Assessment of face specificity in prosopagnosia is hampered by difficulty in gauging pre-morbid expertise for non-face object categories, for which humans vary widely in interest and experience. In this study, we examined the correlation between visual and verbal semantic knowledge for cars to determine if visual recognition accuracy could be predicted from verbal semantic scores. We had 33 healthy subjects and six prosopagnosic patients first rated their own knowledge of cars. They were then given a test of verbal semantic knowledge that presented them with the names of car models, to which they were to match the manufacturer. Lastly, they were given a test of visual recognition, presenting them with images of cars to which they were to provide information at three levels of specificity: model, manufacturer and decade of make. In controls, while self-ratings were only moderately correlated with either visual recognition or verbal semantic knowledge, verbal semantic knowledge was highly correlated with visual recognition, particularly for more specific levels of information. Item concordance showed that less-expert subjects were more likely to provide the most specific information (model name) for the image when they could also match the manufacturer to its name. Prosopagnosic subjects showed reduced visual recognition of cars after adjusting for verbal semantic scores. We conclude that visual recognition is highly correlated with verbal semantic knowledge, that formal measures of verbal semantic knowledge are a more accurate gauge of expertise than self-ratings, and that verbal semantic knowledge can be used to adjust tests of visual recognition for pre-morbid expertise in prosopagnosia.

  20. Visual Crowding: a fundamental limit on conscious perception and object recognition

    PubMed Central

    Whitney, David; Levi, Dennis M.

    2011-01-01

    Crowding, the inability to recognize objects in clutter, sets a fundamental limit on conscious visual perception and object recognition throughout most of the visual field. Despite how widespread and essential it is to object recognition, reading, and visually guided action, a solid operational definition of what crowding is has only recently become clear. The goal of this review is to provide a broad-based synthesis of the most recent findings in this area, to define what crowding is and is not, and to set the stage for future work that will extend crowding well beyond low-level vision. Here we define five diagnostic criteria for what counts as crowding, and further describe factors that both escape and break crowding. All of these lead to the conclusion that crowding occurs at multiple stages in the visual hierarchy. PMID:21420894

  1. False recognition of objects in visual scenes: findings from a combined direct and indirect memory test.

    PubMed

    Weinstein, Yana; Nash, Robert A

    2013-01-01

    We report an extension of the procedure devised by Weinstein and Shanks (Memory & Cognition 36:1415-1428, 2008) to study false recognition and priming of pictures. Participants viewed scenes with multiple embedded objects (seen items), then studied the names of these objects and the names of other objects (read items). Finally, participants completed a combined direct (recognition) and indirect (identification) memory test that included seen items, read items, and new items. In the direct test, participants recognized pictures of seen and read items more often than new pictures. In the indirect test, participants' speed at identifying those same pictures was improved for pictures that they had actually studied, and also for falsely recognized pictures whose names they had read. These data provide new evidence that a false-memory induction procedure can elicit memory-like representations that are difficult to distinguish from "true" memories of studied pictures.

  2. Multiple degree of freedom object recognition using optical relational graph decision nets

    NASA Technical Reports Server (NTRS)

    Casasent, David P.; Lee, Andrew J.

    1988-01-01

    Multiple-degree-of-freedom object recognition concerns objects with no stable rest position with all scale, rotation, and aspect distortions possible. It is assumed that the objects are in a fairly benign background, so that feature extractors are usable. In-plane distortion invariance is provided by use of a polar-log coordinate transform feature space, and out-of-plane distortion invariance is provided by linear discriminant function design. Relational graph decision nets are considered for multiple-degree-of-freedom pattern recognition. The design of Fisher (1936) linear discriminant functions and synthetic discriminant function for use at the nodes of binary and multidecision nets is discussed. Case studies are detailed for two-class and multiclass problems. Simulation results demonstrate the robustness of the processors to quantization of the filter coefficients and to noise.

  3. Improving robustness of speech recognition systems

    NASA Astrophysics Data System (ADS)

    Mitra, Vikramjit

    2010-11-01

    Current Automatic Speech Recognition (ASR) systems fail to perform nearly as good as human speech recognition performance due to their lack of robustness against speech variability and noise contamination. The goal of this dissertation is to investigate these critical robustness issues, put forth different ways to address them and finally present an ASR architecture based upon these robustness criteria. Acoustic variations adversely affect the performance of current phone-based ASR systems, in which speech is modeled as 'beads-on-a-string', where the beads are the individual phone units. While phone units are distinctive in cognitive domain, they are varying in the physical domain and their variation occurs due to a combination of factors including speech style, speaking rate etc.; a phenomenon commonly known as 'coarticulation'. Traditional ASR systems address such coarticulatory variations by using contextualized phone-units such as triphones. Articulatory phonology accounts for coarticulatory variations by modeling speech as a constellation of constricting actions known as articulatory gestures. In such a framework, speech variations such as coarticulation and lenition are accounted for by gestural overlap in time and gestural reduction in space. To realize a gesture-based ASR system, articulatory gestures have to be inferred from the acoustic signal. At the initial stage of this research an initial study was performed using synthetically generated speech to obtain a proof-of-concept that articulatory gestures can indeed be recognized from the speech signal. It was observed that having vocal tract constriction trajectories (TVs) as intermediate representation facilitated the gesture recognition task from the speech signal. Presently no natural speech database contains articulatory gesture annotation; hence an automated iterative time-warping architecture is proposed that can annotate any natural speech database with articulatory gestures and TVs. Two natural

  4. Some consonants sound curvy: effects of sound symbolism on object recognition.

    PubMed

    Aveyard, Mark E

    2012-01-01

    Two experiments explored the influence of consonant sound symbolism on object recognition. In Experiment 1, participants heard a word ostensibly from a foreign language (in reality, a pseudoword) followed by two objects on screen: a rectilinear object and a curvilinear object. The task involved judging which of the two objects was properly described by the unknown pseudoword. The results showed that congruent sound-symbolic pseudoword-object pairs produced higher task accuracy over three rounds of testing than did incongruent pairs, despite the fact that "hard" pseudowords (with three plosives) and "soft" pseudowords (with three nonplosives) were paired equally with rectilinear and curvilinear objects. Experiment 2 reduced awareness of the manipulation by including similar-shaped, target-related distractors. Sound symbolism effects still emerged, though the time course of these effects over three rounds differed from that in Experiment 1.

  5. A biologically inspired neural network model to transformation invariant object recognition

    NASA Astrophysics Data System (ADS)

    Iftekharuddin, Khan M.; Li, Yaqin; Siddiqui, Faraz

    2007-09-01

    Transformation invariant image recognition has been an active research area due to its widespread applications in a variety of fields such as military operations, robotics, medical practices, geographic scene analysis, and many others. The primary goal for this research is detection of objects in the presence of image transformations such as changes in resolution, rotation, translation, scale and occlusion. We investigate a biologically-inspired neural network (NN) model for such transformation-invariant object recognition. In a classical training-testing setup for NN, the performance is largely dependent on the range of transformation or orientation involved in training. However, an even more serious dilemma is that there may not be enough training data available for successful learning or even no training data at all. To alleviate this problem, a biologically inspired reinforcement learning (RL) approach is proposed. In this paper, the RL approach is explored for object recognition with different types of transformations such as changes in scale, size, resolution and rotation. The RL is implemented in an adaptive critic design (ACD) framework, which approximates the neuro-dynamic programming of an action network and a critic network, respectively. Two ACD algorithms such as Heuristic Dynamic Programming (HDP) and Dual Heuristic dynamic Programming (DHP) are investigated to obtain transformation invariant object recognition. The two learning algorithms are evaluated statistically using simulated transformations in images as well as with a large-scale UMIST face database with pose variations. In the face database authentication case, the 90° out-of-plane rotation of faces from 20 different subjects in the UMIST database is used. Our simulations show promising results for both designs for transformation-invariant object recognition and authentication of faces. Comparing the two algorithms, DHP outperforms HDP in learning capability, as DHP takes fewer steps to

  6. Neural mechanisms of infant learning: differences in frontal theta activity during object exploration modulate subsequent object recognition

    PubMed Central

    Begus, Katarina; Southgate, Victoria; Gliga, Teodora

    2015-01-01

    Investigating learning mechanisms in infancy relies largely on behavioural measures like visual attention, which often fail to predict whether stimuli would be encoded successfully. This study explored EEG activity in the theta frequency band, previously shown to predict successful learning in adults, to directly study infants' cognitive engagement, beyond visual attention. We tested 11-month-old infants (N = 23) and demonstrated that differences in frontal theta-band oscillations, recorded during infants' object exploration, predicted differential subsequent recognition of these objects in a preferential-looking test. Given that theta activity is modulated by motivation to learn in adults, these findings set the ground for future investigation into the drivers of infant learning. PMID:26018832

  7. Textons, visual pop-out effects, and object recognition in infancy.

    PubMed

    Rovee-Collier, C; Hankins, E; Bhatt, R

    1992-12-01

    Five experiments were conducted to determine whether primitive perceptual features, or textons, which Julesz (1984) identified in studies of texture segregation with adults, also affect object recognition early in development. Three-month-old infants discriminated Ts and Ls composed of overlapping line segments from +s but not from each other in a delayed-recognition test after 24 hr; however, Ts and Ls were discriminated from each other after only 1 hr. In a priming paradigm, Ts, Ls, and +s were discriminated from one another after 2 weeks. In succeeding experiments, infants exhibited adultlike visual pop-out effects in both delayed recognition and priming paradigms, detecting an L in the midst of 6 +s and vice versa; these effects were symmetrical. The pop-out effects apparently resulted from parallel search: Infants failed to detect 3 Ls among 4 +s. Clearly, some of the same primitive units that have been identified as the building blocks of adult visual perception underlie object recognition early in infancy. PMID:1431738

  8. Object location and object recognition memory impairments, motivation deficits and depression in a model of Gulf War illness.

    PubMed

    Hattiangady, Bharathi; Mishra, Vikas; Kodali, Maheedhar; Shuai, Bing; Rao, Xiolan; Shetty, Ashok K

    2014-01-01

    Memory and mood deficits are the enduring brain-related symptoms in Gulf War illness (GWI). Both animal model and epidemiological investigations have indicated that these impairments in a majority of GW veterans are linked to exposures to chemicals such as pyridostigmine bromide (PB, an antinerve gas drug), permethrin (PM, an insecticide) and DEET (a mosquito repellant) encountered during the Persian Gulf War-1. Our previous study in a rat model has shown that combined exposures to low doses of GWI-related (GWIR) chemicals PB, PM, and DEET with or without 5-min of restraint stress (a mild stress paradigm) causes hippocampus-dependent spatial memory dysfunction in a water maze test (WMT) and increased depressive-like behavior in a forced swim test (FST). In this study, using a larger cohort of rats exposed to GWIR-chemicals and stress, we investigated whether the memory deficiency identified earlier in a WMT is reproducible with an alternative and stress free hippocampus-dependent memory test such as the object location test (OLT). We also ascertained the possible co-existence of hippocampus-independent memory dysfunction using a novel object recognition test (NORT), and alterations in mood function with additional tests for motivation and depression. Our results provide new evidence that exposure to low doses of GWIR-chemicals and mild stress for 4 weeks causes deficits in hippocampus-dependent object location memory and perirhinal cortex-dependent novel object recognition memory. An open field test performed prior to other behavioral analyses revealed that memory impairments were not associated with increased anxiety or deficits in general motor ability. However, behavioral tests for mood function such as a voluntary physical exercise paradigm and a novelty suppressed feeding test (NSFT) demonstrated decreased motivation levels and depression. Thus, exposure to GWIR-chemicals and stress causes both hippocampus-dependent and hippocampus-independent memory

  9. Line-Based Object Recognition using Hausdorff Distance: From Range Images to Molecular Secondary Structure

    SciTech Connect

    Guerra, C; Pascucci, V

    2004-12-13

    Object recognition algorithms are fundamental tools in automatic matching of geometric shapes within a background scene. Many approaches have been proposed in the past to solve the object recognition problem. Two of the key aspects that distinguish them in terms of their practical usability are: (i) the type of input model description and (ii) the comparison criteria used. In this paper we introduce a novel scheme for 3D object recognition based on line segment representation of the input shapes and comparison using the Hausdor distance. This choice of model representation provides the flexibility to apply the scheme in different application areas. We define several variants of the Hausdor distance to compare the models within the framework of well defined metric spaces. We present a matching algorithm that efficiently finds a pattern in a 3D scene. The algorithm approximates a minimization procedure of the Hausdor distance. The output error due to the approximation is guaranteed to be within a known constant bound. Practical results are presented for two classes of objects: (i) polyhedral shapes extracted from segmented range images and (ii) secondary structures of large molecules. In both cases the use of our approximate algorithm allows to match correctly the pattern in the background while achieving the efficiency necessary for practical use of the scheme. In particular the performance is improved substantially with minor degradation of the quality of the matching.

  10. Selective attention affects conceptual object priming and recognition: a study with young and older adults.

    PubMed

    Ballesteros, Soledad; Mayas, Julia

    2014-01-01

    In the present study, we investigated the effects of selective attention at encoding on conceptual object priming (Experiment 1) and old-new recognition memory (Experiment 2) tasks in young and older adults. The procedures of both experiments included encoding and memory test phases separated by a short delay. At encoding, the picture outlines of two familiar objects, one in blue and the other in green, were presented to the left and to the right of fixation. In Experiment 1, participants were instructed to attend to the picture outline of a certain color and to classify the object as natural or artificial. After a short delay, participants performed a natural/artificial speeded conceptual classification task with repeated attended, repeated unattended, and new pictures. In Experiment 2, participants at encoding memorized the attended pictures and classify them as natural or artificial. After the encoding phase, they performed an old-new recognition memory task. Consistent with previous findings with perceptual priming tasks, we found that conceptual object priming, like explicit memory, required attention at encoding. Significant priming was obtained in both age groups, but only for those pictures that were attended at encoding. Although older adults were slower than young adults, both groups showed facilitation for attended pictures. In line with previous studies, young adults had better recognition memory than older adults. PMID:25628588

  11. Selective attention affects conceptual object priming and recognition: a study with young and older adults.

    PubMed

    Ballesteros, Soledad; Mayas, Julia

    2014-01-01

    In the present study, we investigated the effects of selective attention at encoding on conceptual object priming (Experiment 1) and old-new recognition memory (Experiment 2) tasks in young and older adults. The procedures of both experiments included encoding and memory test phases separated by a short delay. At encoding, the picture outlines of two familiar objects, one in blue and the other in green, were presented to the left and to the right of fixation. In Experiment 1, participants were instructed to attend to the picture outline of a certain color and to classify the object as natural or artificial. After a short delay, participants performed a natural/artificial speeded conceptual classification task with repeated attended, repeated unattended, and new pictures. In Experiment 2, participants at encoding memorized the attended pictures and classify them as natural or artificial. After the encoding phase, they performed an old-new recognition memory task. Consistent with previous findings with perceptual priming tasks, we found that conceptual object priming, like explicit memory, required attention at encoding. Significant priming was obtained in both age groups, but only for those pictures that were attended at encoding. Although older adults were slower than young adults, both groups showed facilitation for attended pictures. In line with previous studies, young adults had better recognition memory than older adults.

  12. Selective attention affects conceptual object priming and recognition: a study with young and older adults

    PubMed Central

    Ballesteros, Soledad; Mayas, Julia

    2015-01-01

    In the present study, we investigated the effects of selective attention at encoding on conceptual object priming (Experiment 1) and old–new recognition memory (Experiment 2) tasks in young and older adults. The procedures of both experiments included encoding and memory test phases separated by a short delay. At encoding, the picture outlines of two familiar objects, one in blue and the other in green, were presented to the left and to the right of fixation. In Experiment 1, participants were instructed to attend to the picture outline of a certain color and to classify the object as natural or artificial. After a short delay, participants performed a natural/artificial speeded conceptual classification task with repeated attended, repeated unattended, and new pictures. In Experiment 2, participants at encoding memorized the attended pictures and classify them as natural or artificial. After the encoding phase, they performed an old–new recognition memory task. Consistent with previous findings with perceptual priming tasks, we found that conceptual object priming, like explicit memory, required attention at encoding. Significant priming was obtained in both age groups, but only for those pictures that were attended at encoding. Although older adults were slower than young adults, both groups showed facilitation for attended pictures. In line with previous studies, young adults had better recognition memory than older adults. PMID:25628588

  13. Enhancing The Recognition, Reusability, And Transparency Of Scientific Data Using Digital Object Identifiers

    NASA Astrophysics Data System (ADS)

    Wilson, B. E.; Cook, R. B.; Beaty, T. W.; Lenhardt, W.; Grubb, J.; Hook, L. A.; Sanderson, C.

    2010-12-01

    The Oak Ridge National Laboratory Distributed Active Archive Center for Biogeochemical Dynamics (ORNL DAAC) is part of the NASA Earth Science Data and Information System (ESDIS) project, responsible for archiving and distributing a wide range of terrestrial ecology data sets. Partly to enhance the recognition for scientists sharing their data, the ORNL DAAC has had a data citation policy for many years, with the citation in the name of the scientists who collected and providing an Internet URL pointing to the data set. Some journal editors, however, objected to a URL in a scientific citation, arguing that URL’s are transient and problematic for the anticipated lifetime of a scientific journal article. In response to this concern, the ORNL DAAC started assigning Digital Object Identifiers (DOIs) to published data sets in 2007 and incorporating the DOI in the requested citation for each data set. DOIs have now been assigned to all ORNL DAAC published data sets. Our experience is that the DOI is a very useful tool for finalized data sets, which is most of what the ORNL DAAC deals with and works well for managing data set citations, as well as to data sets that are updated infrequently. We have not assigned DOIs to dynamically generated data sets, such as those generated by our data subsetting tools (such as the MODIS subsetting tool and the dynamic subsets generated by OGC web services). Dynamic data sets may be a case where separating data set identification (for scientific reproducibility) from data set citation (for attribution and impact analysis) may be appropriate. DOIs have also improved our ability to track citations of data sets, both in the formal scientific literature and in documents published to the general Web. We are now seeing examples where researchers are listing published data sets on a curriculum vita, as one indication of improved recognition of the value for sharing and archiving data sets. DOIs are not yet useful for tracking and assessing

  14. ROCIT : a visual object recognition algorithm based on a rank-order coding scheme.

    SciTech Connect

    Gonzales, Antonio Ignacio; Reeves, Paul C.; Jones, John J.; Farkas, Benjamin D.

    2004-06-01

    This document describes ROCIT, a neural-inspired object recognition algorithm based on a rank-order coding scheme that uses a light-weight neuron model. ROCIT coarsely simulates a subset of the human ventral visual stream from the retina through the inferior temporal cortex. It was designed to provide an extensible baseline from which to improve the fidelity of the ventral stream model and explore the engineering potential of rank order coding with respect to object recognition. This report describes the baseline algorithm, the model's neural network architecture, the theoretical basis for the approach, and reviews the history of similar implementations. Illustrative results are used to clarify algorithm details. A formal benchmark to the 1998 FERET fafc test shows above average performance, which is encouraging. The report concludes with a brief review of potential algorithmic extensions for obtaining scale and rotational invariance.

  15. Direction of magnetoencephalography sources associated with feedback and feedforward contributions in a visual object recognition task.

    PubMed

    Ahlfors, Seppo P; Jones, Stephanie R; Ahveninen, Jyrki; Hämäläinen, Matti S; Belliveau, John W; Bar, Moshe

    2015-01-12

    Identifying inter-area communication in terms of the hierarchical organization of functional brain areas is of considerable interest in human neuroimaging. Previous studies have suggested that the direction of magneto- and electroencephalography (MEG, EEG) source currents depend on the layer-specific input patterns into a cortical area. We examined the direction in MEG source currents in a visual object recognition experiment in which there were specific expectations of activation in the fusiform region being driven by either feedforward or feedback inputs. The source for the early non-specific visual evoked response, presumably corresponding to feedforward driven activity, pointed outward, i.e., away from the white matter. In contrast, the source for the later, object-recognition related signals, expected to be driven by feedback inputs, pointed inward, toward the white matter. Associating specific features of the MEG/EEG source waveforms to feedforward and feedback inputs could provide unique information about the activation patterns within hierarchically organized cortical areas.

  16. Multitask joint spatial pyramid matching using sparse representation with dynamic coefficients for object recognition

    NASA Astrophysics Data System (ADS)

    Hajigholam, Mohammad-Hossein; Raie, Abolghasem-Asadollah; Faez, Karim

    2016-03-01

    Object recognition is considered a necessary part in many computer vision applications. Recently, sparse coding methods, based on representing a sparse feature from an image, show remarkable results on several object recognition benchmarks, but the precision obtained by these methods is not yet sufficient. Such a problem arises where there are few training images available. As such, using multiple features and multitask dictionaries appears to be crucial to achieving better results. We use multitask joint sparse representation, using dynamic coefficients to connect these sparse features. In other words, we calculate the importance of each feature for each class separately. This causes the features to be used efficiently and appropriately for each class. Thus, we use variance of features and particle swarm optimization algorithms to obtain these dynamic coefficients. Experimental results of our work on Caltech-101 and Caltech-256 databases show more accuracy compared with state-of-the art ones on the same databases.

  17. Face Memory and Object Recognition in Children with High-Functioning Autism or Asperger Syndrome and in Their Parents

    ERIC Educational Resources Information Center

    Kuusikko-Gauffin, Sanna; Jansson-Verkasalo, Eira; Carter, Alice; Pollock-Wurman, Rachel; Jussila, Katja; Mattila, Marja-Leena; Rahko, Jukka; Ebeling, Hanna; Pauls, David; Moilanen, Irma

    2011-01-01

    Children with Autism Spectrum Disorders (ASDs) have reported to have impairments in face, recognition and face memory, but intact object recognition and object memory. Potential abnormalities, in these fields at the family level of high-functioning children with ASD remains understudied despite, the ever-mounting evidence that ASDs are genetic and…

  18. Preliminary design of a terrain recognition system.

    PubMed

    Zhang, Fan; Fang, Zheng; Liu, Ming; Huang, He

    2011-01-01

    This paper aims to design a wearable terrain recognition system, which might assist the control of powered artificial prosthetic legs. A laser distance sensor and inertial measurement unit (TMU) sensors were mounted on human body. These sensors were used to identify the movement state of the user, reconstruct the geometry of the terrain in front of the user while walking, and recognize the type of terrain before the user stepped on it. Different sensor configurations were investigated and compared. The designed system was evaluated on one healthy human subject when walking on an obstacle course in the laboratory environment. The results showed that the reconstructed terrain height demonstrated clearer pattern difference among studied terrains when the laser was placed on the waist than that when the laser was mounted on the shank. The designed system with the laser on the waist accurately recognized 157 out of 160 tested terrain transitions, 300 ms-2870 ms before the user switched the negotiated terrains. These promising results demonstrated the potential application of the designed terrain recognition system to further improve the control of powered artificial legs.

  19. Exercise improves object recognition memory and induces BDNF expression and cell proliferation in cognitively enriched rats.

    PubMed

    Bechara, R G; Kelly, Á M

    2013-05-15

    Exercise and environmental enrichment are behavioural interventions that have been shown to improve learning and increase neurogenesis in rodents, possibly via neurotrophin-mediated mechanisms. However, many enrichment protocols incorporate exercise, which can itself be viewed as a source of cognitive stimulation in animals housed in standard laboratory conditions. In this experiment we investigate the effect of each intervention separately and in combination on object recognition memory, and analyse associated changes in the dentate gyrus: specifically, in BDNF expression and cell division. We show that both exercise and enrichment improve object recognition memory, but that BDNF mRNA expression and cell proliferation in the dentate gyrus of the hippocampus increase only in exercised rats. These results are in general agreement with recent studies suggesting that the exercise component is the major neurogenic and neurotrophic stimulus in environmental enrichment protocols. We add to the expanding literature several novel aspects including the finding that enrichment in the absence of exercise can improve object recognition memory, probably via mechanisms that are independent of BDNF upregulation and neurogenesis in the dentate gyrus.

  20. Edge detection techniques for iris recognition system

    NASA Astrophysics Data System (ADS)

    Tania, U. T.; Motakabber, S. M. A.; Ibrahimy, M. I.

    2013-12-01

    Nowadays security and authentication are the major parts of our daily life. Iris is one of the most reliable organ or part of human body which can be used for identification and authentication purpose. To develop an iris authentication algorithm for personal identification, this paper examines two edge detection techniques for iris recognition system. Between the Sobel and the Canny edge detection techniques, the experimental result shows that the Canny's technique has better ability to detect points in a digital image where image gray level changes even at slow rate.

  1. On the role of hippocampal protein synthesis in the consolidation and reconsolidation of object recognition memory.

    PubMed

    Rossato, Janine I; Bevilaqua, Lia R M; Myskiw, Jociane C; Medina, Jorge H; Izquierdo, Iván; Cammarota, Martín

    2007-01-01

    Upon retrieval, consolidated memories are again rendered vulnerable to the action of metabolic blockers, notably protein synthesis inhibitors. This has led to the hypothesis that memories are reconsolidated at the time of retrieval, and that this depends on protein synthesis. Ample evidence indicates that the hippocampus plays a key role both in the consolidation and reconsolidation of different memories. Despite this fact, at present there are no studies about the consequences of hippocampal protein synthesis inhibition in the storage and post-retrieval persistence of object recognition memory. Here we report that infusion of the protein synthesis inhibitor anisomycin in the dorsal CA1 region immediately or 180 min but not 360 min after training impairs consolidation of long-term object recognition memory without affecting short-term memory, exploratory behavior, anxiety state, or hippocampal functionality. When given into CA1 after memory reactivation in the presence of familiar objects, ANI did not affect further retention. However, when administered into CA1 immediately after exposing animals to a novel and a familiar object, ANI impaired memory of both of them. The amnesic effect of ANI was long-lasting, did not happen after exposure to two novel objects, following exploration of the context alone, or in the absence of specific stimuli, suggesting that it was not reversible but was contingent on the reactivation of the consolidated trace in the presence of a salient, behaviorally relevant novel cue. Our results indicate that hippocampal protein synthesis is required during a limited post-training time window for consolidation of object recognition memory and show that the hippocampus is engaged during reconsolidation of this type of memory, maybe accruing new information into the original trace.

  2. Cortical Thickness in Fusiform Face Area Predicts Face and Object Recognition Performance.

    PubMed

    McGugin, Rankin W; Van Gulick, Ana E; Gauthier, Isabel

    2016-02-01

    The fusiform face area (FFA) is defined by its selectivity for faces. Several studies have shown that the response of FFA to nonface objects can predict behavioral performance for these objects. However, one possible account is that experts pay more attention to objects in their domain of expertise, driving signals up. Here, we show an effect of expertise with nonface objects in FFA that cannot be explained by differential attention to objects of expertise. We explore the relationship between cortical thickness of FFA and face and object recognition using the Cambridge Face Memory Test and Vanderbilt Expertise Test, respectively. We measured cortical thickness in functionally defined regions in a group of men who evidenced functional expertise effects for cars in FFA. Performance with faces and objects together accounted for approximately 40% of the variance in cortical thickness of several FFA patches. Whereas participants with a thicker FFA cortex performed better with vehicles, those with a thinner FFA cortex performed better with faces and living objects. The results point to a domain-general role of FFA in object perception and reveal an interesting double dissociation that does not contrast faces and objects but rather living and nonliving objects. PMID:26439272

  3. Cortical Thickness in Fusiform Face Area Predicts Face and Object Recognition Performance.

    PubMed

    McGugin, Rankin W; Van Gulick, Ana E; Gauthier, Isabel

    2016-02-01

    The fusiform face area (FFA) is defined by its selectivity for faces. Several studies have shown that the response of FFA to nonface objects can predict behavioral performance for these objects. However, one possible account is that experts pay more attention to objects in their domain of expertise, driving signals up. Here, we show an effect of expertise with nonface objects in FFA that cannot be explained by differential attention to objects of expertise. We explore the relationship between cortical thickness of FFA and face and object recognition using the Cambridge Face Memory Test and Vanderbilt Expertise Test, respectively. We measured cortical thickness in functionally defined regions in a group of men who evidenced functional expertise effects for cars in FFA. Performance with faces and objects together accounted for approximately 40% of the variance in cortical thickness of several FFA patches. Whereas participants with a thicker FFA cortex performed better with vehicles, those with a thinner FFA cortex performed better with faces and living objects. The results point to a domain-general role of FFA in object perception and reveal an interesting double dissociation that does not contrast faces and objects but rather living and nonliving objects.

  4. Cortical Thickness in Fusiform Face Area Predicts Face and Object Recognition Performance

    PubMed Central

    McGugin, Rankin W.; Van Gulick, Ana E.; Gauthier, Isabel

    2016-01-01

    The fusiform face area (FFA) is defined by its selectivity for faces. Several studies have shown that the response of FFA to non-face objects can predict behavioral performance for these objects. However, one possible account is that experts pay more attention to objects in their domain of expertise, driving signals up. Here we show an effect of expertise with non-face objects in FFA that cannot be explained by differential attention to objects of expertise. We explore the relationship between cortical thickness of FFA and face and object recognition using the Cambridge Face Memory Test and Vanderbilt Expertise Test, respectively. We measured cortical thickness in functionally-defined regions in a group of men who evidenced functional expertise effects for cars in FFA. Performance with faces and objects together accounted for approximately 40% of the variance in cortical thickness of several FFA patches. While subjects with a thicker FFA cortex performed better with vehicles, those with a thinner FFA cortex performed better with faces and living objects. The results point to a domain-general role of FFA in object perception and reveal an interesting double dissociation that does not contrast faces and objects, but rather living and non-living objects. PMID:26439272

  5. Recognition of error symptoms in large systems

    NASA Technical Reports Server (NTRS)

    Iyer, Ravishankar K.; Sridhar, V.

    1987-01-01

    A methodology for automatically detecting symptoms of frequently occurring errors in large computer systems is developed. The proposed symptom recognition methodology and its validation are based on probabilistic techniques. The technique is shown to work on real failure data from two CYBER systems at the University of Illinois. The methodology allows for the resolution between independent and dependent causes and, also quantifies a measure of the strength of relationship among errors. Comparison made with failure/repair information obtained from field maintenance engineers shows that in 85% of the cases, the error symptoms recognized by our approach correspond to real system problems. Further, the remaining 15% although not directly supported by field data, were confirmed as valid problems. Some of these were shown to be persistent problems which otherwise would have been considered as minor transients and hence ignored.

  6. DCLOS. Distributed Common Lisp Object System

    SciTech Connect

    Spires, S.

    1997-05-01

    DCLOS provides distributed object capabilities to CLOS. An open implementation distributed object system based upon the Common LISP Object System (CLOS) that supports proxies, copies, replicants, remote method invocation, global object identity, class coercion, object brokering, and other capabilities. Network agents use DCLOS services to exchange messages, to register in a foreign domain, and to provide automatic coercion of objects into proxies, replicants or copies.

  7. Knowledge-based object recognition for different morphological classes of plants

    NASA Astrophysics Data System (ADS)

    Brendel, Thorsten; Schwanke, Joerg; Jensch, Peter F.; Megnet, Roland

    1995-01-01

    Micropropagation of plants is done by cutting juvenile plants and placing them into special container-boxes with nutrient-solution where the pieces can grow up and be cut again several times. To produce high amounts of biomass it is necessary to do plant micropropagation by a robotic syshoot. In this paper we describe parts of the vision syshoot that recognizes plants and their particular cutting points. Therefore, it is necessary to extract elements of the plants and relations between these elements (for example root, shoot, leaf). Different species vary in their morphological appearance, variation is also immanent in plants of the same species. Therefore, we introduce several morphological classes of plants from that we expect same recognition methods. As a result of our work we present rules which help users to create specific algorithms for object recognition of plant species.

  8. Pattern recognition with composite correlation filters designed with multi-object combinatorial optimization

    DOE PAGESBeta

    Awwal, Abdul; Diaz-Ramirez, Victor H.; Cuevas, Andres; Kober, Vitaly; Trujillo, Leonardo

    2014-10-23

    Composite correlation filters are used for solving a wide variety of pattern recognition problems. These filters are given by a combination of several training templates chosen by a designer in an ad hoc manner. In this work, we present a new approach for the design of composite filters based on multi-objective combinatorial optimization. Given a vast search space of training templates, an iterative algorithm is used to synthesize a filter with an optimized performance in terms of several competing criteria. Furthermore, by employing a suggested binary-search procedure a filter bank with a minimum number of filters can be constructed, formore » a prespecified trade-off of performance metrics. Computer simulation results obtained with the proposed method in recognizing geometrically distorted versions of a target in cluttered and noisy scenes are discussed and compared in terms of recognition performance and complexity with existing state-of-the-art filters.« less

  9. Pattern recognition with composite correlation filters designed with multi-object combinatorial optimization

    SciTech Connect

    Awwal, Abdul; Diaz-Ramirez, Victor H.; Cuevas, Andres; Kober, Vitaly; Trujillo, Leonardo

    2014-10-23

    Composite correlation filters are used for solving a wide variety of pattern recognition problems. These filters are given by a combination of several training templates chosen by a designer in an ad hoc manner. In this work, we present a new approach for the design of composite filters based on multi-objective combinatorial optimization. Given a vast search space of training templates, an iterative algorithm is used to synthesize a filter with an optimized performance in terms of several competing criteria. Furthermore, by employing a suggested binary-search procedure a filter bank with a minimum number of filters can be constructed, for a prespecified trade-off of performance metrics. Computer simulation results obtained with the proposed method in recognizing geometrically distorted versions of a target in cluttered and noisy scenes are discussed and compared in terms of recognition performance and complexity with existing state-of-the-art filters.

  10. Automatic TLI recognition system, programmer`s guide

    SciTech Connect

    Lassahn, G.D.

    1997-02-01

    This report describes the software of an automatic target recognition system (version 14), from a programmer`s point of view. The intent is to provide information that will help people who wish to modify the software. In separate volumes are a general description of the ATR system, Automatic TLI Recognition System, General Description, and a user`s manual, Automatic TLI Recognition System, User`s Guide. 2 refs.

  11. The anterior temporal cortex is a primary semantic source of top-down influences on object recognition.

    PubMed

    Chiou, Rocco; Lambon Ralph, Matthew A

    2016-06-01

    Perception emerges from a dynamic interplay between feed-forward sensory input and feedback modulation along the cascade of neural processing. Prior knowledge, a major form of top-down modulatory signal, benefits perception by enabling efficacious inference and resolving ambiguity, particularly under circumstances of degraded visual input. Despite semantic information being a potentially critical source of this top-down influence, to date, the core neural substrate of semantic knowledge (the anterolateral temporal lobe - ATL) has not been considered as a key component of the feedback system. Here we provide direct evidence of its significance for visual cognition - the ATL underpins the semantic aspect of object recognition, amalgamating sensory-based (amount of accumulated sensory input) and semantic-based (representational proximity between exemplars and typicality of appearance) influences. Using transcranial theta-burst stimulation combined with a novel visual identification paradigm, we demonstrate that the left ATL contributes to discrimination between visual objects. Crucially, its contribution is especially vital under situations where semantic knowledge is most needed for supplementing deficiency of input (brief visual exposure), discerning analogously-coded exemplars (close representational distance), and resolving discordance (target appearance violating the statistical typicality of its category). Our findings characterise functional properties of the ATL in object recognition: this neural structure is summoned to augment the visual system when the latter is overtaxed by challenging conditions (insufficient input, overlapped neural coding, and conflict between incoming signal and expected configuration). This suggests a need to revisit current theories of object recognition, incorporating the ATL that interfaces high-level vision with semantic knowledge. PMID:27088615

  12. Naringin and Rutin Alleviates Episodic Memory Deficits in Two Differentially Challenged Object Recognition Tasks

    PubMed Central

    Ramalingayya, Grandhi Venkata; Nampoothiri, Madhavan; Nayak, Pawan G.; Kishore, Anoop; Shenoy, Rekha R.; Mallikarjuna Rao, Chamallamudi; Nandakumar, Krishnadas

    2016-01-01

    Background: Cognitive decline or dementia is a debilitating problem of neurological disorders such as Alzheimer's and Parkinson's disease, including special conditions like chemobrain. Dietary flavonoids proved to be efficacious in delaying the incidence of neurodegenerative diseases. Two such flavonoids, naringin (NAR) and rutin (RUT) were reported to have neuroprotective potential with beneficial effects on spatial and emotional memories in particular. However, the efficacy of these flavonoids is poorly understood on episodic memory, which comprises an important form of autobiographical memory. Objective: This study objective is to evaluate NAR and RUT to reverse time-delay-induced long-term and scopolamine-induced short-term episodic memory deficits in Wistar rats. Materials and Methods: We have evaluated both short-term and long-term episodic memory forms using novel object recognition task. Open field paradigm was used to assess locomotor activity for any confounding influence on memory assessment. Donepezil was used as positive control and was effective in both models at 1 mg/kg, i.p. Results: Animals treated with NAR and RUT at 50 and 100 mg/kg, p.o. spent significantly more time exploring novel object compared to familiar one, whereas control animals spent almost equal time with both objects in choice trial. NAR and RUT dose-dependently increased recognition and discriminative indices in time-induced long-term as well as scopolamine-induced short-term episodic memory deficit models without interfering with the locomotor activity. Conclusion: We conclude that, NAR and RUT averted both short- and long-term episodic memory deficits in Wistar rats, which may be potential interventions for neurodegenerative diseases as well as chemobrain condition. SUMMARY Incidence of Alzheimer's disease is increasing globally and the current therapy is only symptomatic. Curative treatment is a major lacuna. NAR and RUT are natural flavonoids proven for their pleiotropic

  13. An investigation into the cause of orientation-sensitivity in haptic object recognition.

    PubMed

    Lawson, Rebecca

    2011-01-01

    Object orientation influences visual and haptic recognition differently. This could be caused by the two modalities accessing different object representations or it could be due to differences in how each modality acquires information. These two alternatives were investigated using sequential haptic matching tasks. Matches presented the same object twice. Mismatches presented two similarly-shaped objects. Objects were either both placed at the same orientation or were rotated 90° in depth from each other. Experiment 1 manipulated exploration time to test if longer durations weakened orientation-sensitivity by allowing orientation-invariant representations to be extracted. This hypothesis was not supported. Experiment 2 investigated whether the same-orientation advantage resulted from general spatial or motor action cueing rather than the involvement of orientation-specific object representations. To distinguish between these two possibilities, people did a secondary task interleaved within the matching task. They reported the orientation of a fork or spoon which was presented in between the first and second objects. The main axis of the fork/spoon was the same as that of the final object, equating spatial and motor cueing across the same-orientation and orientation-change conditions. Nevertheless, matching remained orientation-sensitive. Together these results suggest that there are separate visual and haptic stored, orientation-specific perceptual representations of objects.

  14. Late development of metric part-relational processing in object recognition.

    PubMed

    Jüttner, Martin; Petters, Dean; Wakui, Elley; Davidoff, Jules

    2014-08-01

    Four experiments with unfamiliar objects examined the remarkably late consolidation of part-relational relative to part-based object recognition (Jüttner, Wakui, Petters, Kaur, & Davidoff, 2013). Our results indicate a particularly protracted developmental trajectory for the processing of metric part relations. Schoolchildren aged 7 to 14 years and adults were tested in 3-Alternative-Forced-Choice tasks to judge the correct appearance of upright and inverted newly learned multipart objects that had been manipulated in terms of individual parts or part relations. Experiment 1 showed that even the youngest tested children were close to adult levels of performance for recognizing categorical changes of individual parts and relative part position. By contrast, Experiment 2 demonstrated that performance for detecting metric changes of relative part position was distinctly reduced in young children compared with recognizing metric changes of individual parts, and did not approach the latter until 11 to 12 years. A similar developmental dissociation was observed in Experiment 3, which contrasted the detection of metric relative-size changes and metric part changes. Experiment 4 showed that manipulations of metric size that were perceived as part (rather than part-relational) changes eliminated this dissociation. Implications for theories of object recognition and similarities to the development of face perception are discussed.

  15. Principal component analysis in the wavelet domain: new features for underwater object recognition

    NASA Astrophysics Data System (ADS)

    Okimoto, Gordon S.; Lemonds, David W.

    1999-08-01

    Principal component analysis (PCA) in the wavelet domain provides powerful features for underwater object recognition applications. The multiresolution analysis of the Morlet wavelet transform (MWT) is used to pre-process echo returns from targets ensonified by biologically motivated broadband signal. PCA is then used to compress and denoise the resulting time-scale signal representation for presentation to a hierarchical neural network for object classification. Wavelet/PCA features combined with multi-aspect data fusion and neural networks have resulted in impressive underwater object recognition performance using backscatter data generated by simulate dolphin echolocation clicks and bat- like linear frequency modulated upsweeps. For example, wavelet/PCA features extracted from LFM echo returns have resulted in correct classification rates of 98.6 percent over a six target suite, which includes two mine simulators and four clutter objects. For the same data, ROC analysis of the two-class mine-like versus non-mine-like problem resulted in a probability of detection of 0.981 and a probability of false alarm of 0.032 at the 'optimal' operating point. The wavelet/PCA feature extraction algorithm is currently being implemented in VLSI for use in small, unmanned underwater vehicles designed for mine- hunting operations in shallow water environments.

  16. Object recognition memory and BDNF expression are reduced in young TgCRND8 mice

    PubMed Central

    Francis, Beverly M.; Kim, John; Barakat, Meredith E.; Fraenkl, Stephan; Yücel, Yeni H.; Peng, Shiyong; Michalski, Bernadeta; Fahnestock, Margaret; McLaurin, JoAnne; Mount, Howard T.J.

    2012-01-01

    The TgCRND8 mouse model of Alzheimer’s disease exhibits progressive cortical and hippocampal β-amyloid accumulation, resulting in plaque pathology and spatial memory impairment by 3 months of age. We tested whether TgCRND8 cognitive function is disrupted prior to the appearance of macroscopic plaques in an object recognition task. We found profound deficits in 8-week-old mice. Animals this age were not impaired on the Morris water maze task. TgCRND8 and littermate controls did not differ in their duration of object exploration or optokinetic responses. Thus, visual and motor dysfunction did not confound the phenotype. Object memory deficits point to the frontal cortex and hippocampus as early targets of functional disruption. Indeed, we observed altered levels of brain-derived neurotrophic factor (BDNF) messenger ribonucleic acid (mRNA) in these brain regions of preplaque TgCRND8 mice. Our findings suggest that object recognition provides an early index of cognitive impairment associated with amyloid exposure and reduced brain-derived neurotrophic factor expression in the TgCRND8 mouse. PMID:20447730

  17. The evolution of meaning: spatio-temporal dynamics of visual object recognition.

    PubMed

    Clarke, Alex; Taylor, Kirsten I; Tyler, Lorraine K

    2011-08-01

    Research on the spatio-temporal dynamics of visual object recognition suggests a recurrent, interactive model whereby an initial feedforward sweep through the ventral stream to prefrontal cortex is followed by recurrent interactions. However, critical questions remain regarding the factors that mediate the degree of recurrent interactions necessary for meaningful object recognition. The novel prediction we test here is that recurrent interactivity is driven by increasing semantic integration demands as defined by the complexity of semantic information required by the task and driven by the stimuli. To test this prediction, we recorded magnetoencephalography data while participants named living and nonliving objects during two naming tasks. We found that the spatio-temporal dynamics of neural activity were modulated by the level of semantic integration required. Specifically, source reconstructed time courses and phase synchronization measures showed increased recurrent interactions as a function of semantic integration demands. These findings demonstrate that the cortical dynamics of object processing are modulated by the complexity of semantic information required from the visual input. PMID:20617883

  18. Guppies Show Behavioural but Not Cognitive Sex Differences in a Novel Object Recognition Test.

    PubMed

    Lucon-Xiccato, Tyrone; Dadda, Marco

    2016-01-01

    The novel object recognition (NOR) test is a widely-used paradigm to study learning and memory in rodents. NOR performance is typically measured as the preference to interact with a novel object over a familiar object based on spontaneous exploratory behaviour. In rats and mice, females usually have greater NOR ability than males. The NOR test is now available for a large number of species, including fish, but sex differences have not been properly tested outside of rodents. We compared male and female guppies (Poecilia reticulata) in a NOR test to study whether sex differences exist also for fish. We focused on sex differences in both performance and behaviour of guppies during the test. In our experiment, adult guppies expressed a preference for the novel object as most rodents and other species do. When we looked at sex differences, we found the two sexes showed a similar preference for the novel object over the familiar object, suggesting that male and female guppies have similar NOR performances. Analysis of behaviour revealed that males were more inclined to swim in the proximity of the two objects than females. Further, males explored the novel object at the beginning of the experiment while females did so afterwards. These two behavioural differences are possibly due to sex differences in exploration. Even though NOR performance is not different between male and female guppies, the behavioural sex differences we found could affect the results of the experiments and should be carefully considered when assessing fish memory with the NOR test. PMID:27305102

  19. Guppies Show Behavioural but Not Cognitive Sex Differences in a Novel Object Recognition Test

    PubMed Central

    Lucon-Xiccato, Tyrone; Dadda, Marco

    2016-01-01

    The novel object recognition (NOR) test is a widely-used paradigm to study learning and memory in rodents. NOR performance is typically measured as the preference to interact with a novel object over a familiar object based on spontaneous exploratory behaviour. In rats and mice, females usually have greater NOR ability than males. The NOR test is now available for a large number of species, including fish, but sex differences have not been properly tested outside of rodents. We compared male and female guppies (Poecilia reticulata) in a NOR test to study whether sex differences exist also for fish. We focused on sex differences in both performance and behaviour of guppies during the test. In our experiment, adult guppies expressed a preference for the novel object as most rodents and other species do. When we looked at sex differences, we found the two sexes showed a similar preference for the novel object over the familiar object, suggesting that male and female guppies have similar NOR performances. Analysis of behaviour revealed that males were more inclined to swim in the proximity of the two objects than females. Further, males explored the novel object at the beginning of the experiment while females did so afterwards. These two behavioural differences are possibly due to sex differences in exploration. Even though NOR performance is not different between male and female guppies, the behavioural sex differences we found could affect the results of the experiments and should be carefully considered when assessing fish memory with the NOR test. PMID:27305102

  20. Effect of perinatal asphyxia on tuberomammillary nucleus neuronal density and object recognition memory: A possible role for histamine?

    PubMed

    Flores-Balter, Gabriela; Cordova-Jadue, Héctor; Chiti-Morales, Alessandra; Lespay, Carolyne; Espina-Marchant, Pablo; Falcon, Romina; Grinspun, Noemi; Sanchez, Jessica; Bustamante, Diego; Morales, Paola; Herrera-Marschitz, Mario; Valdés, José L

    2016-10-15

    Perinatal asphyxia (PA) is associated with long-term neuronal damage and cognitive deficits in adulthood, such as learning and memory disabilities. After PA, specific brain regions are compromised, including neocortex, hippocampus, basal ganglia, and ascending neuromodulatory pathways, such as dopamine system, explaining some of the cognitive disabilities. We hypothesize that other neuromodulatory systems, such as histamine system from the tuberomammillary nucleus (TMN), which widely project to telencephalon, shown to be relevant for learning and memory, may be compromised by PA. We investigated here the effect of PA on (i) Density and neuronal activity of TMN neurons by double immunoreactivity for adenosine deaminase (ADA) and c-Fos, as marker for histaminergic neurons and neuronal activity respectively. (ii) Expression of the histamine-synthesizing enzyme, histidine decarboxylase (HDC) by western blot and (iii) thioperamide an H3 histamine receptor antagonist, on an object recognition memory task. Asphyxia-exposed rats showed a decrease of ADA density and c-Fos activity in TMN, and decrease of HDC expression in hypothalamus. Asphyxia-exposed rats also showed a low performance in object recognition memory compared to caesarean-delivered controls, which was reverted in a dose-dependent manner by the H3 antagonist thioperamide (5-10mg/kg, i.p.). The present results show that the histaminergic neuronal system of the TMN is involved in the long-term effects induced by PA, affecting learning and memory. PMID:27444242

  1. Effect of perinatal asphyxia on tuberomammillary nucleus neuronal density and object recognition memory: A possible role for histamine?

    PubMed

    Flores-Balter, Gabriela; Cordova-Jadue, Héctor; Chiti-Morales, Alessandra; Lespay, Carolyne; Espina-Marchant, Pablo; Falcon, Romina; Grinspun, Noemi; Sanchez, Jessica; Bustamante, Diego; Morales, Paola; Herrera-Marschitz, Mario; Valdés, José L

    2016-10-15

    Perinatal asphyxia (PA) is associated with long-term neuronal damage and cognitive deficits in adulthood, such as learning and memory disabilities. After PA, specific brain regions are compromised, including neocortex, hippocampus, basal ganglia, and ascending neuromodulatory pathways, such as dopamine system, explaining some of the cognitive disabilities. We hypothesize that other neuromodulatory systems, such as histamine system from the tuberomammillary nucleus (TMN), which widely project to telencephalon, shown to be relevant for learning and memory, may be compromised by PA. We investigated here the effect of PA on (i) Density and neuronal activity of TMN neurons by double immunoreactivity for adenosine deaminase (ADA) and c-Fos, as marker for histaminergic neurons and neuronal activity respectively. (ii) Expression of the histamine-synthesizing enzyme, histidine decarboxylase (HDC) by western blot and (iii) thioperamide an H3 histamine receptor antagonist, on an object recognition memory task. Asphyxia-exposed rats showed a decrease of ADA density and c-Fos activity in TMN, and decrease of HDC expression in hypothalamus. Asphyxia-exposed rats also showed a low performance in object recognition memory compared to caesarean-delivered controls, which was reverted in a dose-dependent manner by the H3 antagonist thioperamide (5-10mg/kg, i.p.). The present results show that the histaminergic neuronal system of the TMN is involved in the long-term effects induced by PA, affecting learning and memory.

  2. Human-inspired sound environment recognition system for assistive vehicles

    NASA Astrophysics Data System (ADS)

    González Vidal, Eduardo; Fredes Zarricueta, Ernesto; Auat Cheein, Fernando

    2015-02-01

    Objective. The human auditory system acquires environmental information under sound stimuli faster than visual or touch systems, which in turn, allows for faster human responses to such stimuli. It also complements senses such as sight, where direct line-of-view is necessary to identify objects, in the environment recognition process. This work focuses on implementing human reaction to sound stimuli and environment recognition on assistive robotic devices, such as robotic wheelchairs or robotized cars. These vehicles need environment information to ensure safe navigation. Approach. In the field of environment recognition, range sensors (such as LiDAR and ultrasonic systems) and artificial vision devices are widely used; however, these sensors depend on environment constraints (such as lighting variability or color of objects), and sound can provide important information for the characterization of an environment. In this work, we propose a sound-based approach to enhance the environment recognition process, mainly for cases that compromise human integrity, according to the International Classification of Functioning (ICF). Our proposal is based on a neural network implementation that is able to classify up to 15 different environments, each selected according to the ICF considerations on environment factors in the community-based physical activities of people with disabilities. Main results. The accuracy rates in environment classification ranges from 84% to 93%. This classification is later used to constrain assistive vehicle navigation in order to protect the user during daily activities. This work also includes real-time outdoor experimentation (performed on an assistive vehicle) by seven volunteers with different disabilities (but without cognitive impairment and experienced in the use of wheelchairs), statistical validation, comparison with previously published work, and a discussion section where the pros and cons of our system are evaluated. Significance

  3. Physical exercise during adolescence versus adulthood: differential effects on object recognition memory and brain-derived neurotrophic factor levels.

    PubMed

    Hopkins, M E; Nitecki, R; Bucci, D J

    2011-10-27

    It is well established that physical exercise can enhance hippocampal-dependent forms of learning and memory in laboratory animals, commensurate with increases in hippocampal neural plasticity (brain-derived neurotrophic factor [BDNF] mRNA/protein, neurogenesis, long-term potentiation [LTP]). However, very little is known about the effects of exercise on other, non-spatial forms of learning and memory. In addition, there has been little investigation of the duration of the effects of exercise on behavior or plasticity. Likewise, few studies have compared the effects of exercising during adulthood versus adolescence. This is particularly important since exercise may capitalize on the peak of neural plasticity observed during adolescence, resulting in a different pattern of behavioral and neurobiological effects. The present study addressed these gaps in the literature by comparing the effects of 4 weeks of voluntary exercise (wheel running) during adulthood or adolescence on novel object recognition and BDNF levels in the perirhinal cortex (PER) and hippocampus (HP). Exercising during adulthood improved object recognition memory when rats were tested immediately after 4 weeks of exercise, an effect that was accompanied by increased BDNF levels in PER and HP. When rats were tested again 2 weeks after exercise ended, the effects of exercise on recognition memory and BDNF levels were no longer present. Exercising during adolescence had a very different pattern of effects. First, both exercising and non-exercising rats could discriminate between novel and familiar objects immediately after the exercise regimen ended; furthermore there was no group difference in BDNF levels. Two or four weeks later, however, rats that had previously exercised as adolescents could still discriminate between novel and familiar objects, while non-exercising rats could not. Moreover, the formerly exercising rats exhibited higher levels of BDNF in PER compared to HP, while the reverse was

  4. Simulated Prosthetic Vision: The Benefits of Computer-Based Object Recognition and Localization.

    PubMed

    Macé, Marc J-M; Guivarch, Valérian; Denis, Grégoire; Jouffrais, Christophe

    2015-07-01

    Clinical trials with blind patients implanted with a visual neuroprosthesis showed that even the simplest tasks were difficult to perform with the limited vision restored with current implants. Simulated prosthetic vision (SPV) is a powerful tool to investigate the putative functions of the upcoming generations of visual neuroprostheses. Recent studies based on SPV showed that several generations of implants will be required before usable vision is restored. However, none of these studies relied on advanced image processing. High-level image processing could significantly reduce the amount of information required to perform visual tasks and help restore visuomotor behaviors, even with current low-resolution implants. In this study, we simulated a prosthetic vision device based on object localization in the scene. We evaluated the usability of this device for object recognition, localization, and reaching. We showed that a very low number of electrodes (e.g., nine) are sufficient to restore visually guided reaching movements with fair timing (10 s) and high accuracy. In addition, performance, both in terms of accuracy and speed, was comparable with 9 and 100 electrodes. Extraction of high level information (object recognition and localization) from video images could drastically enhance the usability of current visual neuroprosthesis. We suggest that this method-that is, localization of targets of interest in the scene-may restore various visuomotor behaviors. This method could prove functional on current low-resolution implants. The main limitation resides in the reliability of the vision algorithms, which are improving rapidly.

  5. On the Relation between Face and Object Recognition in Developmental Prosopagnosia: No Dissociation but a Systematic Association

    PubMed Central

    Klargaard, Solja K.; Starrfelt, Randi

    2016-01-01

    There is an ongoing debate about whether face recognition and object recognition constitute separate domains. Clarification of this issue can have important theoretical implications as face recognition is often used as a prime example of domain-specificity in mind and brain. An important source of input to this debate comes from studies of individuals with developmental prosopagnosia, suggesting that face recognition can be selectively impaired. We put the selectivity hypothesis to test by assessing the performance of 10 individuals with developmental prosopagnosia on demanding tests of visual object processing involving both regular and degraded drawings. None of the individuals exhibited a clear dissociation between face and object recognition, and as a group they were significantly more affected by degradation of objects than control participants. Importantly, we also find positive correlations between the severity of the face recognition impairment and the degree of impaired performance with degraded objects. This suggests that the face and object deficits are systematically related rather than coincidental. We conclude that at present, there is no strong evidence in the literature on developmental prosopagnosia supporting domain-specific accounts of face recognition. PMID:27792780

  6. Dopamine D1 receptor stimulation modulates the formation and retrieval of novel object recognition memory: Role of the prelimbic cortex

    PubMed Central

    Pezze, Marie A.; Marshall, Hayley J.; Fone, Kevin C.F.; Cassaday, Helen J.

    2015-01-01

    Previous studies have shown that dopamine D1 receptor antagonists impair novel object recognition memory but the effects of dopamine D1 receptor stimulation remain to be determined. This study investigated the effects of the selective dopamine D1 receptor agonist SKF81297 on acquisition and retrieval in the novel object recognition task in male Wistar rats. SKF81297 (0.4 and 0.8 mg/kg s.c.) given 15 min before the sampling phase impaired novel object recognition evaluated 10 min or 24 h later. The same treatments also reduced novel object recognition memory tested 24 h after the sampling phase and when given 15 min before the choice session. These data indicate that D1 receptor stimulation modulates both the encoding and retrieval of object recognition memory. Microinfusion of SKF81297 (0.025 or 0.05 μg/side) into the prelimbic sub-region of the medial prefrontal cortex (mPFC) in this case 10 min before the sampling phase also impaired novel object recognition memory, suggesting that the mPFC is one important site mediating the effects of D1 receptor stimulation on visual recognition memory. PMID:26277743

  7. The Role of Sensory-Motor Information in Object Recognition: Evidence from Category-Specific Visual Agnosia

    ERIC Educational Resources Information Center

    Wolk, D.A.; Coslett, H.B.; Glosser, G.

    2005-01-01

    The role of sensory-motor representations in object recognition was investigated in experiments involving AD, a patient with mild visual agnosia who was impaired in the recognition of visually presented living as compared to non-living entities. AD named visually presented items for which sensory-motor information was available significantly more…

  8. Assessment of disease-related cognitive impairments using the novel object recognition (NOR) task in rodents.

    PubMed

    Grayson, Ben; Leger, Marianne; Piercy, Chloe; Adamson, Lisa; Harte, Michael; Neill, Joanna C

    2015-05-15

    The novel object recognition test (NOR) test is a two trial cognitive paradigm that assesses recognition memory. Recognition memory is disturbed in a range of human disorders and NOR is widely used in rodents for investigating deficits in a variety of animal models of human conditions where cognition is impaired. It possesses several advantages over more complex tasks that involve lengthy training procedures and/or food or water deprivation. It is quick to administer, non-rewarded, provides data quickly, cost effective and most importantly, ethologically relevant as it relies on the animal's natural preference for novelty. A PubMed search revealed over 900 publications in rats and mice using this task over the past 3 years with 34 reviews in the past 10 years, demonstrating its increasing popularity with neuroscientists. Although it is widely used in many disparate areas of research, no articles have systematically examined this to date, which is the subject of our review. We reveal that NOR may be used to study recognition memory deficits that occur in Alzheimer's disease and schizophrenia, where research is extensive, in Parkinson's disease and Autism Spectrum Disorders (ASD) where we observed markedly reduced numbers of publications. In addition, we review the use of NOR to study cognitive deficits induced by traumatic brain injury and cancer chemotherapy, not disorders per se, but situations in which cognitive deficits dramatically reduce the quality of life for those affected, see Fig. 1 for a summary. Our review reveals that, in all these animal models, the NOR test is extremely useful for identification of the cognitive deficits observed, their neural basis, and for testing the efficacy of novel therapeutic agents. Our conclusion is that NOR is of considerable value for cognitive researchers of all disciplines and we anticipate that its use will continue to increase due to its versatility and several other advantages, as detailed in this review.

  9. Recognition of metal cations by biological systems.

    PubMed

    Truter, M R

    1975-11-01

    Recognition of metal cations by biological systems can be compared with the geochemical criteria for isomorphous replacement. Biological systems are more highly selective and much more rapid. Methods of maintaining an optimum concentration, including storage and transfer for the essential trace elements, copper and iron, used in some organisms are in part reproducible by coordination chemists while other features have not been reporduced in models. Poisoning can result from a foreign metal taking part in a reaction irreversibly so that the recognition site or molecule is not released. For major nutrients, sodium, potassium, magnesium and calcium, there are similarities to the trace metals in selective uptake but differences qualitatively and quantitatively in biological activity. Compounds selective for potassium replace all the solvation sphere with a symmetrical arrangement of oxygen atoms; those selective for sodium give an asymmetrical environment with retention of a solvent molecule. Experiments with naturally occurring antibiotics and synthetic model compounds have shown that flexibility is an important feature of selectivity and that for transfer or carrier properties there is an optimum (as opposed to a maximum) metal-ligand stability constant. Thallium is taken up instead of potassium and will activate some enzymes; it is suggested that the poisonous characteristics arise because the thallium ion may bind more strongly than potassium to part of a site and then fail to bind additional atoms as required for the biological activity. Criteria for the design of selective complexing agents are given with indications of those which might transfer more than one metal at once. PMID:1815

  10. Developing a Credit Recognition System for Chinese Higher Education Institutions

    ERIC Educational Resources Information Center

    Li, Fuhui

    2015-01-01

    In recent years, a credit recognition system has been developing in Chinese higher education institutions. Much research has been done on this development, but it has been concentrated on system building, barriers/issues and international practices. The relationship between credit recognition system reforms and democratisation of higher education…

  11. Face Recognition System with Holographic Memory and Stereovision Technology

    NASA Astrophysics Data System (ADS)

    Honma, Satoshi; Yagisawa, Yasuaki; Momose, Hidetomo; Sekiguchi, Toru

    2011-09-01

    We have proposed a face recognition system with holographic memory and stereovision technology (FARSHAS). In this system, facial three-dimensional data is captured by stereovision technology and then the facial images at a position in front of the virtual camera is reconstructed automatically. Using the corrected facial images, we estimated theoretically the error rate of the facial recognition system.

  12. The research of edge extraction and target recognition based on inherent feature of objects

    NASA Astrophysics Data System (ADS)

    Xie, Yu-chan; Lin, Yu-chi; Huang, Yin-guo

    2008-03-01

    Current research on computer vision often needs specific techniques for particular problems. Little use has been made of high-level aspects of computer vision, such as three-dimensional (3D) object recognition, that are appropriate for large classes of problems and situations. In particular, high-level vision often focuses mainly on the extraction of symbolic descriptions, and pays little attention to the speed of processing. In order to extract and recognize target intelligently and rapidly, in this paper we developed a new 3D target recognition method based on inherent feature of objects in which cuboid was taken as model. On the basis of analysis cuboid nature contour and greyhound distributing characteristics, overall fuzzy evaluating technique was utilized to recognize and segment the target. Then Hough transform was used to extract and match model's main edges, we reconstruct aim edges by stereo technology in the end. There are three major contributions in this paper. Firstly, the corresponding relations between the parameters of cuboid model's straight edges lines in an image field and in the transform field were summed up. By those, the aimless computations and searches in Hough transform processing can be reduced greatly and the efficiency is improved. Secondly, as the priori knowledge about cuboids contour's geometry character known already, the intersections of the component extracted edges are taken, and assess the geometry of candidate edges matches based on the intersections, rather than the extracted edges. Therefore the outlines are enhanced and the noise is depressed. Finally, a 3-D target recognition method is proposed. Compared with other recognition methods, this new method has a quick response time and can be achieved with high-level computer vision. The method present here can be used widely in vision-guide techniques to strengthen its intelligence and generalization, which can also play an important role in object tracking, port AGV, robots

  13. An automatic geo-spatial object recognition algorithm for high resolution satellite images

    NASA Astrophysics Data System (ADS)

    Ergul, Mustafa; Alatan, A. Aydın.

    2013-10-01

    This paper proposes a novel automatic geo-spatial object recognition algorithm for high resolution satellite imaging. The proposed algorithm consists of two main steps; a hypothesis generation step with a local feature-based algorithm and a verification step with a shape-based approach. In the hypothesis generation step, a set of hypothesis for possible object locations is generated, aiming lower missed detections and higher false-positives by using a Bag of Visual Words type approach. In the verification step, the foreground objects are first extracted by a semi-supervised image segmentation algorithm, utilizing detection results from the previous step, and then, the shape descriptors for segmented objects are utilized to prune out the false positives. Based on simulation results, it can be argued that the proposed algorithm achieves both high precision and high recall rates as a result of taking advantage of both the local feature-based and the shape-based object detection approaches. The superiority of the proposed method is due to the ability of minimization of false alarm rate and since most of the object shapes contain more characteristic and discriminative information about their identity and functionality.

  14. A new behavioural apparatus to reduce animal numbers in multiple types of spontaneous object recognition paradigms in rats.

    PubMed

    Ameen-Ali, K E; Eacott, M J; Easton, A

    2012-10-15

    Standard object recognition procedures assess animals' memory through their spontaneous exploration of novel objects or novel configurations of objects with other aspects of their environment. Such tasks are widely used in memory research, but also in pharmaceutical companies screening new drug treatments. However, behaviour in these tasks may be driven by influences other than novelty such as stress from handling which can subsequently influence performance. This extra-experimental variance means that large numbers of animals are required to maintain power. In addition, accumulation of data is time consuming as animals typically perform only one trial per day. The present study aimed to explore how effectively recognition memory could be tested with a new continual trials apparatus which allows for multiple trials within a session and reduced handling stress through combining features of delayed nonmatching-to-sample and spontaneous object recognition tasks. In this apparatus Lister hooded rats displayed performance significantly above chance levels in object recognition tasks (Experiments 1 and 2) and in tasks of object-location (Experiment 3) and object-in-context memory (Experiment 4) with data from only five animals or fewer per experimental group. The findings indicated that the results were comparable to those of previous reports in the literature and maintained statistical power whilst using less than a third of the number of animals typically used in spontaneous recognition paradigms. Overall, the results highlight the potential benefit of the continual trials apparatus to reduce the number of animals used in recognition memory tasks.

  15. Conversion of short-term to long-term memory in the novel object recognition paradigm.

    PubMed

    Moore, Shannon J; Deshpande, Kaivalya; Stinnett, Gwen S; Seasholtz, Audrey F; Murphy, Geoffrey G

    2013-10-01

    It is well-known that stress can significantly impact learning; however, whether this effect facilitates or impairs the resultant memory depends on the characteristics of the stressor. Investigation of these dynamics can be confounded by the role of the stressor in motivating performance in a task. Positing a cohesive model of the effect of stress on learning and memory necessitates elucidating the consequences of stressful stimuli independently from task-specific functions. Therefore, the goal of this study was to examine the effect of manipulating a task-independent stressor (elevated light level) on short-term and long-term memory in the novel object recognition paradigm. Short-term memory was elicited in both low light and high light conditions, but long-term memory specifically required high light conditions during the acquisition phase (familiarization trial) and was independent of the light level during retrieval (test trial). Additionally, long-term memory appeared to be independent of stress-mediated glucocorticoid release, as both low and high light produced similar levels of plasma corticosterone, which further did not correlate with subsequent memory performance. Finally, both short-term and long-term memory showed no savings between repeated experiments suggesting that this novel object recognition paradigm may be useful for longitudinal studies, particularly when investigating treatments to stabilize or enhance weak memories in neurodegenerative diseases or during age-related cognitive decline.

  16. Remembering the object you fear: brain potentials during recognition of spiders in spider-fearful individuals.

    PubMed

    Michalowski, Jaroslaw M; Weymar, Mathias; Hamm, Alfons O

    2014-01-01

    In the present study we investigated long-term memory for unpleasant, neutral and spider pictures in 15 spider-fearful and 15 non-fearful control individuals using behavioral and electrophysiological measures. During the initial (incidental) encoding, pictures were passively viewed in three separate blocks and were subsequently rated for valence and arousal. A recognition memory task was performed one week later in which old and new unpleasant, neutral and spider pictures were presented. Replicating previous results, we found enhanced memory performance and higher confidence ratings for unpleasant when compared to neutral materials in both animal fearful individuals and controls. When compared to controls high animal fearful individuals also showed a tendency towards better memory accuracy and significantly higher confidence during recognition of spider pictures, suggesting that memory of objects prompting specific fear is also facilitated in fearful individuals. In line, spider-fearful but not control participants responded with larger ERP positivity for correctly recognized old when compared to correctly rejected new spider pictures, thus showing the same effects in the neural signature of emotional memory for feared objects that were already discovered for other emotional materials. The increased fear memory for phobic materials observed in the present study in spider-fearful individuals might result in an enhanced fear response and reinforce negative beliefs aggravating anxiety symptomatology and hindering recovery.

  17. Different roles for M1 and M2 receptors within perirhinal cortex in object recognition and discrimination.

    PubMed

    Bartko, Susan J; Winters, Boyer D; Saksida, Lisa M; Bussey, Timothy J

    2014-04-01

    Recognition and discrimination of objects and individuals are critical cognitive faculties in both humans and non-human animals, and cholinergic transmission has been shown to be essential for both of these functions. In the present study we focused on the role of M1 and M2 muscarinic receptors in perirhinal cortex (PRh)-dependent object recognition and discrimination. The selective M1 antagonists pirenzepine and the snake toxin MT-7, and a selective M2 antagonist, AF-DX 116, were infused directly into PRh. Pre-sample infusions of both pirenzepine and AF-DX 116 significantly impaired object recognition memory in a delay-dependent manner. However, pirenzepine and MT-7, but not AF-DX 116, impaired oddity discrimination performance in a perceptual difficulty-dependent manner. The findings indicate distinct functions for M1 and M2 receptors in object recognition and discrimination.

  18. A road sign detection and recognition system for mobile devices

    NASA Astrophysics Data System (ADS)

    Xiong, Bo; Izmirli, Ozgur

    2012-01-01

    We present an automatic road sign detection and recognition service system for mobile devices. The system is based on a client-server architecture which allows mobile users to take pictures of road signs and request detection and recognition service from a centralized server for processing. The preprocessing, detection and recognition take place at the server end and consequently, the result is sent back to the mobile device. For road sign detection, we use particular color features calculated from the input image. Recognition is implemented using a neural network based on normalized color histogram features. We report on the effects of various parameters on recognition accuracy. Our results demonstrate that the system can provide an efficient framework for locale-dependent road sign recognition with multilingual support.

  19. Random-profiles-based 3D face recognition system.

    PubMed

    Kim, Joongrock; Yu, Sunjin; Lee, Sangyoun

    2014-01-01

    In this paper, a noble nonintrusive three-dimensional (3D) face modeling system for random-profile-based 3D face recognition is presented. Although recent two-dimensional (2D) face recognition systems can achieve a reliable recognition rate under certain conditions, their performance is limited by internal and external changes, such as illumination and pose variation. To address these issues, 3D face recognition, which uses 3D face data, has recently received much attention. However, the performance of 3D face recognition highly depends on the precision of acquired 3D face data, while also requiring more computational power and storage capacity than 2D face recognition systems. In this paper, we present a developed nonintrusive 3D face modeling system composed of a stereo vision system and an invisible near-infrared line laser, which can be directly applied to profile-based 3D face recognition. We further propose a novel random-profile-based 3D face recognition method that is memory-efficient and pose-invariant. The experimental results demonstrate that the reconstructed 3D face data consists of more than 50 k 3D point clouds and a reliable recognition rate against pose variation.

  20. Random-Profiles-Based 3D Face Recognition System

    PubMed Central

    Joongrock, Kim; Sunjin, Yu; Sangyoun, Lee

    2014-01-01

    In this paper, a noble nonintrusive three-dimensional (3D) face modeling system for random-profile-based 3D face recognition is presented. Although recent two-dimensional (2D) face recognition systems can achieve a reliable recognition rate under certain conditions, their performance is limited by internal and external changes, such as illumination and pose variation. To address these issues, 3D face recognition, which uses 3D face data, has recently received much attention. However, the performance of 3D face recognition highly depends on the precision of acquired 3D face data, while also requiring more computational power and storage capacity than 2D face recognition systems. In this paper, we present a developed nonintrusive 3D face modeling system composed of a stereo vision system and an invisible near-infrared line laser, which can be directly applied to profile-based 3D face recognition. We further propose a novel random-profile-based 3D face recognition method that is memory-efficient and pose-invariant. The experimental results demonstrate that the reconstructed 3D face data consists of more than 50 k 3D point clouds and a reliable recognition rate against pose variation. PMID:24691101

  1. Recognition of 3-D symmetric objects from range images in automated assembly tasks

    NASA Technical Reports Server (NTRS)

    Alvertos, Nicolas; Dcunha, Ivan

    1990-01-01

    A new technique is presented for the three dimensional recognition of symmetric objects from range images. Beginning from the implicit representation of quadrics, a set of ten coefficients is determined for symmetric objects like spheres, cones, cylinders, ellipsoids, and parallelepipeds. Instead of using these ten coefficients trying to fit them to smooth surfaces (patches) based on the traditional way of determining curvatures, a new approach based on two dimensional geometry is used. For each symmetric object, a unique set of two dimensional curves is obtained from the various angles at which the object is intersected with a plane. Using the same ten coefficients obtained earlier and based on the discriminant method, each of these curves is classified as a parabola, circle, ellipse, or hyperbola. Each symmetric object is found to possess a unique set of these two dimensional curves whereby it can be differentiated from the others. It is shown that instead of using the three dimensional discriminant which involves evaluation of the rank of its matrix, it is sufficient to use the two dimensional discriminant which only requires three arithmetic operations.

  2. Novel object recognition ability in female mice following exposure to nanoparticle-rich diesel exhaust

    SciTech Connect

    Win-Shwe, Tin-Tin; Fujimaki, Hidekazu; Fujitani, Yuji; Hirano, Seishiro

    2012-08-01

    Recently, our laboratory reported that exposure to nanoparticle-rich diesel exhaust (NRDE) for 3 months impaired hippocampus-dependent spatial learning ability and up-regulated the expressions of memory function-related genes in the hippocampus of female mice. However, whether NRDE affects the hippocampus-dependent non-spatial learning ability and the mechanism of NRDE-induced neurotoxicity was unknown. Female BALB/c mice were exposed to clean air, middle-dose NRDE (M-NRDE, 47 μg/m{sup 3}), high-dose NRDE (H-NRDE, 129 μg/m{sup 3}), or filtered H-NRDE (F-DE) for 3 months. We then investigated the effect of NRDE exposure on non-spatial learning ability and the expression of genes related to glutamate neurotransmission using a novel object recognition test and a real-time RT-PCR analysis, respectively. We also examined microglia marker Iba1 immunoreactivity in the hippocampus using immunohistochemical analyses. Mice exposed to H-NRDE or F-DE could not discriminate between familiar and novel objects. The control and M-NRDE-exposed groups showed a significantly increased discrimination index, compared to the H-NRDE-exposed group. Although no significant changes in the expression levels of the NMDA receptor subunits were observed, the expression of glutamate transporter EAAT4 was decreased and that of glutamic acid decarboxylase GAD65 was increased in the hippocampus of H-NRDE-exposed mice, compared with the expression levels in control mice. We also found that microglia activation was prominent in the hippocampal area of the H-NRDE-exposed mice, compared with the other groups. These results indicated that exposure to NRDE for 3 months impaired the novel object recognition ability. The present study suggests that genes related to glutamate metabolism may be involved in the NRDE-induced neurotoxicity observed in the present mouse model. -- Highlights: ► The effects of nanoparticle-induced neurotoxicity remain unclear. ► We investigated the effect of exposure to

  3. Hybrid gesture recognition system for short-range use

    NASA Astrophysics Data System (ADS)

    Minagawa, Akihiro; Fan, Wei; Katsuyama, Yutaka; Takebe, Hiroaki; Ozawa, Noriaki; Hotta, Yoshinobu; Sun, Jun

    2012-03-01

    In recent years, various gesture recognition systems have been studied for use in television and video games[1]. In such systems, motion areas ranging from 1 to 3 meters deep have been evaluated[2]. However, with the burgeoning popularity of small mobile displays, gesture recognition systems capable of operating at much shorter ranges have become necessary. The problems related to such systems are exacerbated by the fact that the camera's field of view is unknown to the user during operation, which imposes several restrictions on his/her actions. To overcome the restrictions generated from such mobile camera devices, and to create a more flexible gesture recognition interface, we propose a hybrid hand gesture system, in which two types of gesture recognition modules are prepared and with which the most appropriate recognition module is selected by a dedicated switching module. The two recognition modules of this system are shape analysis using a boosting approach (detection-based approach)[3] and motion analysis using image frame differences (motion-based approach)(for example, see[4]). We evaluated this system using sample users and classified the resulting errors into three categories: errors that depend on the recognition module, errors caused by incorrect module identification, and errors resulting from user actions. In this paper, we show the results of our investigations and explain the problems related to short-range gesture recognition systems.

  4. A Comparison of the Effects of Depth Rotation on Visual and Haptic Three-Dimensional Object Recognition

    ERIC Educational Resources Information Center

    Lawson, Rebecca

    2009-01-01

    A sequential matching task was used to compare how the difficulty of shape discrimination influences the achievement of object constancy for depth rotations across haptic and visual object recognition. Stimuli were nameable, 3-dimensional plastic models of familiar objects (e.g., bed, chair) and morphs midway between these endpoint shapes (e.g., a…

  5. Fingerprint recognition system by use of graph matching

    NASA Astrophysics Data System (ADS)

    Shen, Wei; Shen, Jun; Zheng, Huicheng

    2001-09-01

    Fingerprint recognition is an important subject in biometrics to identify or verify persons by physiological characteristics, and has found wide applications in different domains. In the present paper, we present a finger recognition system that combines singular points and structures. The principal steps of processing in our system are: preprocessing and ridge segmentation, singular point extraction and selection, graph representation, and finger recognition by graphs matching. Our fingerprint recognition system is implemented and tested for many fingerprint images and the experimental result are satisfactory. Different techniques are used in our system, such as fast calculation of orientation field, local fuzzy dynamical thresholding, algebraic analysis of connections and fingerprints representation and matching by graphs. Wed find that for fingerprint database that is not very large, the recognition rate is very high even without using a prior coarse category classification. This system works well for both one-to-few and one-to-many problems.

  6. Detection of prosodics by using a speech recognition system

    NASA Astrophysics Data System (ADS)

    Hupp, N. A.

    1991-07-01

    The problem was to determine the ability of a speech recognizer to extract prosodic speech features, such as pitch and stress, and to examine these features for application to future voice recognition systems. The Speech Systems Incorporated (SSI) speech recognizer demonstrated that it could detect prosodic features and that these features do indicate the word and/or syllable that is stressed by the speaker. The research examined the effect of prosodics, such as pitch, amplitude, and duration, on word and syllable stress by using the SSI. Subjects read phases and sentences, using a given intonation and stress. The three sections of the experiment compared questions and answers, words stressed within a sentence, and noun/verb pairs, such as object and subject. The results were analyzed both on the syllable level and the word level. In all cases, there was a significant increase in pitch, amplitude, and duration when comparing stressed words and syllables to unstressed words and syllables. When comparing unstressed words only, it was also noted that the first word in a sentence has an increase in pitch, amplitude, and duration. The threshold could be set in recognition systems for each of these parameters. Current speech recognizers do not use acoustic data above the word level. This research shows that we have the capability of developing better speech systems by incorporating prosodics with new linguistic software.

  7. Heterozygous Che-1 KO mice show deficiencies in object recognition memory persistence.

    PubMed

    Zalcman, Gisela; Corbi, Nicoletta; Di Certo, Maria Grazia; Mattei, Elisabetta; Federman, Noel; Romano, Arturo

    2016-10-01

    Transcriptional regulation is a key process in the formation of long-term memories. Che-1 is a protein involved in the regulation of gene transcription that has recently been proved to bind the transcription factor NF-κB, which is known to be involved in many memory-related molecular events. This evidence prompted us to investigate the putative role of Che-1 in memory processes. For this study we newly generated a line of Che-1(+/-) heterozygous mice. Che-1 homozygous KO mouse is lethal during development, but Che-1(+/-) heterozygous mouse is normal in its general anatomical and physiological characteristics. We analyzed the behavioral characteristic and memory performance of Che-1(+/-) mice in two NF-κB dependent types of memory. We found that Che-1(+/-) mice show similar locomotor activity and thigmotactic behavior than wild type (WT) mice in an open field. In a similar way, no differences were found in anxiety-like behavior between Che-1(+/-) and WT mice in an elevated plus maze as well as in fear response in a contextual fear conditioning (CFC) and object exploration in a novel object recognition (NOR) task. No differences were found between WT and Che-1(+/-) mice performance in CFC training and when tested at 24h or 7days after training. Similar performance was found between groups in NOR task, both in training and 24h testing performance. However, we found that object recognition memory persistence at 7days was impaired in Che-1(+/-) heterozygous mice. This is the first evidence showing that Che-1 is involved in memory processes. PMID:27589891

  8. Heterozygous Che-1 KO mice show deficiencies in object recognition memory persistence.

    PubMed

    Zalcman, Gisela; Corbi, Nicoletta; Di Certo, Maria Grazia; Mattei, Elisabetta; Federman, Noel; Romano, Arturo

    2016-10-01

    Transcriptional regulation is a key process in the formation of long-term memories. Che-1 is a protein involved in the regulation of gene transcription that has recently been proved to bind the transcription factor NF-κB, which is known to be involved in many memory-related molecular events. This evidence prompted us to investigate the putative role of Che-1 in memory processes. For this study we newly generated a line of Che-1(+/-) heterozygous mice. Che-1 homozygous KO mouse is lethal during development, but Che-1(+/-) heterozygous mouse is normal in its general anatomical and physiological characteristics. We analyzed the behavioral characteristic and memory performance of Che-1(+/-) mice in two NF-κB dependent types of memory. We found that Che-1(+/-) mice show similar locomotor activity and thigmotactic behavior than wild type (WT) mice in an open field. In a similar way, no differences were found in anxiety-like behavior between Che-1(+/-) and WT mice in an elevated plus maze as well as in fear response in a contextual fear conditioning (CFC) and object exploration in a novel object recognition (NOR) task. No differences were found between WT and Che-1(+/-) mice performance in CFC training and when tested at 24h or 7days after training. Similar performance was found between groups in NOR task, both in training and 24h testing performance. However, we found that object recognition memory persistence at 7days was impaired in Che-1(+/-) heterozygous mice. This is the first evidence showing that Che-1 is involved in memory processes.

  9. Assessing rodent hippocampal involvement in the novel object recognition task. A review.

    PubMed

    Cohen, Sarah J; Stackman, Robert W

    2015-05-15

    The novel object recognition (NOR) task has emerged as a popular method for testing the neurobiology of nonspatial memory in rodents. This task exploits the natural tendency of rodents to explore novel items and depending on the amount of time that rodents spend exploring the presented objects, inferences about memory can be established. Despite its wide use, the underlying neural circuitry and mechanisms supporting NOR have not been clearly defined. In particular, considerable debate has focused on whether the hippocampus plays a significant role in the object memory that is encoded, consolidated and then retrieved during discrete stages of the NOR task. Here we analyzed the results of all published reports in which the role of the rodent hippocampus in object memory was inferred from performance in the task with restricted parameters. We note that the remarkable variability in NOR methods across studies complicates the ability to draw meaningful conclusions from the work. Focusing on 12 reports in which a minimum criterion of sample session object exploration was imposed, we find that temporary or permanent lesion of the hippocampus consistently disrupts object memory when a delay of 10 min or greater is imposed between the sample and test sessions. We discuss the significance of a delay-dependent role of the hippocampus in NOR within the framework of the medial temporal lobe. We assert that standardization of the NOR protocol is essential for obtaining reliable data that can then be compared across studies to build consensus as to the specific contribution of the rodent hippocampus to object memory.

  10. System and method for disrupting suspect objects

    DOEpatents

    Gladwell, T. Scott; Garretson, Justin R; Hobart, Clinton G; Monda, Mark J

    2013-07-09

    A system and method for disrupting at least one component of a suspect object is provided. The system includes a source for passing radiation through the suspect object, a screen for receiving the radiation passing through the suspect object and generating at least one image therefrom, a weapon having a discharge deployable therefrom, and a targeting unit. The targeting unit displays the image(s) of the suspect object and aims the weapon at a disruption point on the displayed image such that the weapon may be positioned to deploy the discharge at the disruption point whereby the suspect object is disabled.

  11. Site change detection and object recognition using thermophysical affine invariants from infrared imagery

    NASA Astrophysics Data System (ADS)

    Nandhakumar, Nagaraj; Michel, Johnathan D.; Arnold, D. Gregory; Velten, Vincent J.

    1995-09-01

    Research on the formulation of invariant features for model-based object recognition has mostly been concerned with geometric constructs either of the object or in the imaging process. We describe a new method that identifies invariant features computed from long wave infrared imagery. These features are called thermophysical invariants and depend primarily on the material composition of the object. We use this approach for identifying objects or changes in scenes viewed by downward looking infrared images. Features are defined that are functions of only the thermophysical properties of the imaged materials. A physics-based model is derived from the principle of conservation of energy applied at the surface of the imaged regions. A linear form of the model is used to derive features that remain constant despite changes in scene parameters/driving conditions. Simulated and real imagery, as well as ground truth thermo-couple measurements were used to test the behavior of such features. A method of change detection in outdoor scenes is investigated. The invariants are used to detect when a hypothesized material no longer exists at a given location. For example, one can detect when a patch of clay/gravel has been replaced with concrete at a given site.

  12. Subsurface object recognition by means of regularization techniques for mapping coastal waters floor

    NASA Astrophysics Data System (ADS)

    Jiménez-Rodríguez, Luis O.; Umana-Diaz, Alejandra; Diaz-Santos, Jose; Neira-Carolina, Gerardino; Morales-Morales, Javier; Rodriguez, Eladio

    2005-10-01

    A fundamental challenge to Remote Sensing is mapping the ocean floor in coastal shallow waters where variability, due to the interaction between the coast and the sea, can bring significant disparity in the optical properties of the water column. The objects to be detected, coral reefs, sands and submerged aquatic vegetation, have weak signals, with temporal and spatial variation. In real scenarios the absorption and backscattering coefficients have spatial variation due to different sources of variability (river discharge, different depths of shallow waters, water currents) and temporal fluctuations. This paper presents the development of algorithms for retrieving information and its application to the recognition, classification and mapping of objects under coastal shallow waters. A mathematical model that simplifies the radiative transfer equation was used to quantify the interaction between the object of interest, the medium and the sensor. The retrieval of information requires the development of mathematical models and processing tools in the area of inversion, image reconstruction and detection. The algorithms developed were applied to one set of remotely sensed data: a high resolution HYPERION hyperspectral imagery. An inverse problem arises as this spectral data is used for mapping the ocean shallow waters floor. Tikhonov method of regularization was used in the inversion process to estimate the bottom albedo of the ocean floor using a priori information in the form of stored spectral signatures, previously measured, of objects of interest, such as sand, corals, and sea grass.

  13. Effects of heavy particle irradiation and diet on object recognition memory in rats

    NASA Astrophysics Data System (ADS)

    Rabin, Bernard M.; Carrihill-Knoll, Kirsty; Hinchman, Marie; Shukitt-Hale, Barbara; Joseph, James A.; Foster, Brian C.

    2009-04-01

    On long-duration missions to other planets astronauts will be exposed to types and doses of radiation that are not experienced in low earth orbit. Previous research using a ground-based model for exposure to cosmic rays has shown that exposure to heavy particles, such as 56Fe, disrupts spatial learning and memory measured using the Morris water maze. Maintaining rats on diets containing antioxidant phytochemicals for 2 weeks prior to irradiation ameliorated this deficit. The present experiments were designed to determine: (1) the generality of the particle-induced disruption of memory by examining the effects of exposure to 56Fe particles on object recognition memory; and (2) whether maintaining rats on these antioxidant diets for 2 weeks prior to irradiation would also ameliorate any potential deficit. The results showed that exposure to low doses of 56Fe particles does disrupt recognition memory and that maintaining rats on antioxidant diets containing blueberry and strawberry extract for only 2 weeks was effective in ameliorating the disruptive effects of irradiation. The results are discussed in terms of the mechanisms by which exposure to these particles may produce effects on neurocognitive performance.

  14. Embedded Palmprint Recognition System Using OMAP 3530

    PubMed Central

    Shen, Linlin; Wu, Shipei; Zheng, Songhao; Ji, Zhen

    2012-01-01

    We have proposed in this paper an embedded palmprint recognition system using the dual-core OMAP 3530 platform. An improved algorithm based on palm code was proposed first. In this method, a Gabor wavelet is first convolved with the palmprint image to produce a response image, where local binary patterns are then applied to code the relation among the magnitude of wavelet response at the ccentral pixel with that of its neighbors. The method is fully tested using the public PolyU palmprint database. While palm code achieves only about 89% accuracy, over 96% accuracy is achieved by the proposed G-LBP approach. The proposed algorithm was then deployed to the DSP processor of OMAP 3530 and work together with the ARM processor for feature extraction. When complicated algorithms run on the DSP processor, the ARM processor can focus on image capture, user interface and peripheral control. Integrated with an image sensing module and central processing board, the designed device can achieve accurate and real time performance. PMID:22438721

  15. Retrieval is not necessary to trigger reconsolidation of object recognition memory in the perirhinal cortex

    PubMed Central

    Santoyo-Zedillo, Marianela; Rodriguez-Ortiz, Carlos J.; Chavez-Marchetta, Gianfranco; Bermudez-Rattoni, Federico

    2014-01-01

    Memory retrieval has been considered as requisite to initiate memory reconsolidation; however, some studies indicate that blocking retrieval does not prevent memory from undergoing reconsolidation. Since N-methyl-D-aspartate (NMDA) and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) glutamate receptors in the perirhinal cortex have been involved in object recognition memory formation, the present study evaluated whether retrieval and reconsolidation are independent processes by manipulating these glutamate receptors. The results showed that AMPA receptor antagonist infusions in the perirhinal cortex blocked retrieval, but did not affect memory reconsolidation, although NMDA receptor antagonist infusions disrupted reconsolidation even if retrieval was blocked. Importantly, neither of these antagonists disrupted short-term memory. These data suggest that memory underwent reconsolidation even in the absence of retrieval. PMID:25128536

  16. The influence of print exposure on the body-object interaction effect in visual word recognition

    PubMed Central

    Hansen, Dana; Siakaluk, Paul D.; Pexman, Penny M.

    2012-01-01

    We examined the influence of print exposure on the body-object interaction (BOI) effect in visual word recognition. High print exposure readers and low print exposure readers either made semantic categorizations (“Is the word easily imageable?”; Experiment 1) or phonological lexical decisions (“Does the item sound like a real English word?”; Experiment 2). The results from Experiment 1 showed that there was a larger BOI effect for the low print exposure readers than for the high print exposure readers in semantic categorization, though an effect was observed for both print exposure groups. However, the results from Experiment 2 showed that the BOI effect was observed only for the high print exposure readers in phonological lexical decision. The results of the present study suggest that print exposure does influence the BOI effect, and that this influence varies as a function of task demands. PMID:22563312

  17. Pattern Recognition Methods and Features Selection for Speech Emotion Recognition System

    PubMed Central

    Partila, Pavol; Voznak, Miroslav; Tovarek, Jaromir

    2015-01-01

    The impact of the classification method and features selection for the speech emotion recognition accuracy is discussed in this paper. Selecting the correct parameters in combination with the classifier is an important part of reducing the complexity of system computing. This step is necessary especially for systems that will be deployed in real-time applications. The reason for the development and improvement of speech emotion recognition systems is wide usability in nowadays automatic voice controlled systems. Berlin database of emotional recordings was used in this experiment. Classification accuracy of artificial neural networks, k-nearest neighbours, and Gaussian mixture model is measured considering the selection of prosodic, spectral, and voice quality features. The purpose was to find an optimal combination of methods and group of features for stress detection in human speech. The research contribution lies in the design of the speech emotion recognition system due to its accuracy and efficiency. PMID:26346654

  18. An Evaluation of PC-Based Optical Character Recognition Systems.

    ERIC Educational Resources Information Center

    Schreier, E. M.; Uslan, M. M.

    1991-01-01

    The review examines six personal computer-based optical character recognition (OCR) systems designed for use by blind and visually impaired people. Considered are OCR components and terms, documentation, scanning and reading, command structure, conversion, unique features, accuracy of recognition, scanning time, speed, and cost. (DB)

  19. [Non-conscious perception of emotional faces affects the visual objects recognition].

    PubMed

    Gerasimenko, N Iu; Slavutskaia, A V; Kalinin, S A; Mikhaĭlova, E S

    2013-01-01

    In 34 healthy subjects we have analyzed accuracy and reaction time (RT) during the recognition of complex visual images: pictures of animals and non-living objects. The target stimuli were preceded by brief presentation of masking non-target ones, which represented drawings of emotional (angry, fearful, happy) or neutral faces. We have revealed that in contrast to accuracy the RT depended on the emotional expression of the preceding faces. RT was significantly shorter if the target objects were paired with the angry and fearful faces as compared with the happy and neutral ones. These effects depended on the category of the target stimulus and were more prominent for objects than for animals. Further, the emotional faces' effects were determined by emotional and communication personality traits (defined by Cattell's Questionnaire) and were clearer defined in more sensitive, anxious and pessimistic introverts. The data are important for understanding the mechanisms of human visual behavior determination by non-consciously processing of emotional information. PMID:23885550

  20. Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance.

    PubMed

    Majaj, Najib J; Hong, Ha; Solomon, Ethan A; DiCarlo, James J

    2015-09-30

    To go beyond qualitative models of the biological substrate of object recognition, we ask: can a single ventral stream neuronal linking hypothesis quantitatively account for core object recognition performance over a broad range of tasks? We measured human performance in 64 object recognition tests using thousands of challenging images that explore shape similarity and identity preserving object variation. We then used multielectrode arrays to measure neuronal population responses to those same images in visual areas V4 and inferior temporal (IT) cortex of monkeys and simulated V1 population responses. We tested leading candidate linking hypotheses and control hypotheses, each postulating how ventral stream neuronal responses underlie object recognition behavior. Specifically, for each hypothesis, we computed the predicted performance on the 64 tests and compared it with the measured pattern of human performance. All tested hypotheses based on low- and mid-level visually evoked activity (pixels, V1, and V4) were very poor predictors of the human behavioral pattern. However, simple learned weighted sums of distributed average IT firing rates exactly predicted the behavioral pattern. More elaborate linking hypotheses relying on IT trial-by-trial correlational structure, finer IT temporal codes, or ones that strictly respect the known spatial substructures of IT ("face patches") did not improve predictive power. Although these results do not reject those more elaborate hypotheses, they suggest a simple, sufficient quantitative model: each object recognition task is learned from the spatially distributed mean firing rates (100 ms) of ∼60,000 IT neurons and is executed as a simple weighted sum of those firing rates. Significance statement: We sought to go beyond qualitative models of visual object recognition and determine whether a single neuronal linking hypothesis can quantitatively account for core object recognition behavior. To achieve this, we designed a

  1. Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance

    PubMed Central

    Hong, Ha; Solomon, Ethan A.; DiCarlo, James J.

    2015-01-01

    To go beyond qualitative models of the biological substrate of object recognition, we ask: can a single ventral stream neuronal linking hypothesis quantitatively account for core object recognition performance over a broad range of tasks? We measured human performance in 64 object recognition tests using thousands of challenging images that explore shape similarity and identity preserving object variation. We then used multielectrode arrays to measure neuronal population responses to those same images in visual areas V4 and inferior temporal (IT) cortex of monkeys and simulated V1 population responses. We tested leading candidate linking hypotheses and control hypotheses, each postulating how ventral stream neuronal responses underlie object recognition behavior. Specifically, for each hypothesis, we computed the predicted performance on the 64 tests and compared it with the measured pattern of human performance. All tested hypotheses based on low- and mid-level visually evoked activity (pixels, V1, and V4) were very poor predictors of the human behavioral pattern. However, simple learned weighted sums of distributed average IT firing rates exactly predicted the behavioral pattern. More elaborate linking hypotheses relying on IT trial-by-trial correlational structure, finer IT temporal codes, or ones that strictly respect the known spatial substructures of IT (“face patches”) did not improve predictive power. Although these results do not reject those more elaborate hypotheses, they suggest a simple, sufficient quantitative model: each object recognition task is learned from the spatially distributed mean firing rates (100 ms) of ∼60,000 IT neurons and is executed as a simple weighted sum of those firing rates. SIGNIFICANCE STATEMENT We sought to go beyond qualitative models of visual object recognition and determine whether a single neuronal linking hypothesis can quantitatively account for core object recognition behavior. To achieve this, we designed a

  2. Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance.

    PubMed

    Majaj, Najib J; Hong, Ha; Solomon, Ethan A; DiCarlo, James J

    2015-09-30

    To go beyond qualitative models of the biological substrate of object recognition, we ask: can a single ventral stream neuronal linking hypothesis quantitatively account for core object recognition performance over a broad range of tasks? We measured human performance in 64 object recognition tests using thousands of challenging images that explore shape similarity and identity preserving object variation. We then used multielectrode arrays to measure neuronal population responses to those same images in visual areas V4 and inferior temporal (IT) cortex of monkeys and simulated V1 population responses. We tested leading candidate linking hypotheses and control hypotheses, each postulating how ventral stream neuronal responses underlie object recognition behavior. Specifically, for each hypothesis, we computed the predicted performance on the 64 tests and compared it with the measured pattern of human performance. All tested hypotheses based on low- and mid-level visually evoked activity (pixels, V1, and V4) were very poor predictors of the human behavioral pattern. However, simple learned weighted sums of distributed average IT firing rates exactly predicted the behavioral pattern. More elaborate linking hypotheses relying on IT trial-by-trial correlational structure, finer IT temporal codes, or ones that strictly respect the known spatial substructures of IT ("face patches") did not improve predictive power. Although these results do not reject those more elaborate hypotheses, they suggest a simple, sufficient quantitative model: each object recognition task is learned from the spatially distributed mean firing rates (100 ms) of ∼60,000 IT neurons and is executed as a simple weighted sum of those firing rates. Significance statement: We sought to go beyond qualitative models of visual object recognition and determine whether a single neuronal linking hypothesis can quantitatively account for core object recognition behavior. To achieve this, we designed a

  3. Introducing AN Agent-Based Object Recognition Operator for Proximity Analysis

    NASA Astrophysics Data System (ADS)

    Behzadi, S.; Ali. Alesheikh, A.

    2013-09-01

    Object selection is a basic procedure in a Geographic Information System (GIS). Most current methods for doing so, select objects in two phases: create a simple distance-bounded geometric buffer; and intersect it with available features. This paper introduces a novel and intelligent selection operator based on the autonomy of the agent-based approach. The proposed operator recognizes objects around one object only in one step. In the proposed approach, each point object acts as an agent-automata object. It then senses its vicinity and identifies the surrounding objects. To assess the proposed model, the operator is designed, implemented, and evaluated in a case study. Finally, the results are evaluated and presented in details in the paper.

  4. Infusion of protein synthesis inhibitors in the entorhinal cortex blocks consolidation but not reconsolidation of object recognition memory.

    PubMed

    Lima, Ramón H; Rossato, Janine I; Furini, Cristiane R; Bevilaqua, Lia R; Izquierdo, Iván; Cammarota, Martín

    2009-05-01

    Memory consolidation and reconsolidation require the induction of protein synthesis in some areas of the brain. Here, we show that infusion of the protein synthesis inhibitors anisomycin, emetine and cycloheximide in the entorhinal cortex immediately but not 180 min or 360 min after training in an object recognition learning task hinders long-term memory retention without affecting short-term memory or behavioral performance. Inhibition of protein synthesis in the entorhinal cortex after memory reactivation involving either a combination of familiar and novel objects or two familiar objects does not affect retention. Our data suggest that protein synthesis in the entorhinal cortex is necessary early after training for consolidation of object recognition memory. However, inhibition of protein synthesis in this cortical region after memory retrieval does not seem to affect the stability of the recognition trace.

  5. A self-organized learning strategy for object recognition by an embedded line of attraction

    NASA Astrophysics Data System (ADS)

    Seow, Ming-Jung; Alex, Ann T.; Asari, Vijayan K.

    2012-04-01

    For humans, a picture is worth a thousand words, but to a machine, it is just a seemingly random array of numbers. Although machines are very fast and efficient, they are vastly inferior to humans for everyday information processing. Algorithms that mimic the way the human brain computes and learns may be the solution. In this paper we present a theoretical model based on the observation that images of similar visual perceptions reside in a complex manifold in an image space. The perceived features are often highly structured and hidden in a complex set of relationships or high-dimensional abstractions. To model the pattern manifold, we present a novel learning algorithm using a recurrent neural network. The brain memorizes information using a dynamical system made of interconnected neurons. Retrieval of information is accomplished in an associative sense. It starts from an arbitrary state that might be an encoded representation of a visual image and converges to another state that is stable. The stable state is what the brain remembers. In designing a recurrent neural network, it is usually of prime importance to guarantee the convergence in the dynamics of the network. We propose to modify this picture: if the brain remembers by converging to the state representing familiar patterns, it should also diverge from such states when presented with an unknown encoded representation of a visual image belonging to a different category. That is, the identification of an instability mode is an indication that a presented pattern is far away from any stored pattern and therefore cannot be associated with current memories. These properties can be used to circumvent the plasticity-stability dilemma by using the fluctuating mode as an indicator to create new states. We capture this behavior using a novel neural architecture and learning algorithm, in which the system performs self-organization utilizing a stability mode and an instability mode for the dynamical system. Based

  6. Cellular Phone Face Recognition System Based on Optical Phase Correlation

    NASA Astrophysics Data System (ADS)

    Watanabe, Eriko; Ishikawa, Sayuri; Ohta, Maiko; Kodate, Kashiko

    We propose a high security facial recognition system using a cellular phone on the mobile network. This system is composed of a face recognition engine based on optical phase correlation which uses phase information with emphasis on a Fourier domain, a control sever and the cellular phone with a compact camera for taking pictures, as a portable terminal. Compared with various correlation methods, our face recognition engine revealed the most accurate EER of less than 1%. By using the JAVA interface on this system, we implemented the stable system taking pictures, providing functions to prevent spoofing while transferring images. This recognition system was tested on 300 women students and the results proved this system effective.

  7. Schlieren system and method for moving objects

    NASA Technical Reports Server (NTRS)

    Weinstein, Leonard M. (Inventor)

    1996-01-01

    A system and method are provided for recording density changes in a flow field surrounding a moving object. A mask having an aperture for regulating the passage of images therethrough is placed in front of an image recording medium. An optical system is placed in front of the mask. A transition having a light field-of-view and a dark field-of-view is located beyond the test object. The optical system focuses an image of the transition at the mask such that the aperture causes a band of light to be defined on the image recording medium. The optical system further focuses an image of the object through the aperture of the mask so that the image of the object appears on the image recording medium. Relative motion is minimized between the mask and the transition. Relative motion is also minimized between the image recording medium and the image of the object. In this way, the image of the object and density changes in a flow field surrounding the object are recorded on the image recording medium when the object crosses the transition in front of the optical system.

  8. Remote weapon station for automatic target recognition system demand analysis

    NASA Astrophysics Data System (ADS)

    Lei, Zhang; Li, Sheng-cai; Shi, Cai

    2015-08-01

    Introduces a remote weapon station basic composition and the main advantage, analysis of target based on image automatic recognition system for remote weapon station of practical significance, the system elaborated the image based automatic target recognition system in the photoelectric stabilized technology, multi-sensor image fusion technology, integrated control target image enhancement, target behavior risk analysis technology, intelligent based on the character of the image automatic target recognition algorithm research, micro sensor technology as the key technology of the development in the field of demand.

  9. Change detection of built-up land: A framework of combining pixel-based detection and object-based recognition

    NASA Astrophysics Data System (ADS)

    Xiao, Pengfeng; Zhang, Xueliang; Wang, Dongguang; Yuan, Min; Feng, Xuezhi; Kelly, Maggi

    2016-09-01

    This study proposed a new framework that combines pixel-level change detection and object-level recognition to detect changes of built-up land from high-spatial resolution remote sensing images. First, an adaptive differencing method was designed to detect changes at the pixel level based on both spectral and textural features. Next, the changed pixels were subjected to a set of morphological operations to improve the completeness and to generate changed objects, achieving the transition of change detection from the pixel level to the object level. The changed objects were further recognised through the difference of morphological building index in two phases to indicate changed objects on built-up land. The transformation from changed pixels to changed objects makes the proposed framework distinct with both the pixel-based and the object-based change detection methods. Compared with the pixel-based methods, the proposed framework can improve the change detection capability through the transformation and successive recognition of objects. Compared with the object-based method, the proposed framework avoids the issue of multitemporal segmentation and can generate changed objects directly from changed pixels. The experimental results show the effectiveness of the transformation from changed pixels to changed objects and the successive object-based recognition on improving the detection accuracy, which justify the application potential of the proposed change detection framework.

  10. Parallel object-oriented decision tree system

    DOEpatents

    Kamath; Chandrika , Cantu-Paz; Erick

    2006-02-28

    A data mining decision tree system that uncovers patterns, associations, anomalies, and other statistically significant structures in data by reading and displaying data files, extracting relevant features for each of the objects, and using a method of recognizing patterns among the objects based upon object features through a decision tree that reads the data, sorts the data if necessary, determines the best manner to split the data into subsets according to some criterion, and splits the data.

  11. Object recognition based on Google's reverse image search and image similarity

    NASA Astrophysics Data System (ADS)

    Horváth, András.

    2015-12-01

    Image classification is one of the most challenging tasks in computer vision and a general multiclass classifier could solve many different tasks in image processing. Classification is usually done by shallow learning for predefined objects, which is a difficult task and very different from human vision, which is based on continuous learning of object classes and one requires years to learn a large taxonomy of objects which are not disjunct nor independent. In this paper I present a system based on Google image similarity algorithm and Google image database, which can classify a large set of different objects in a human like manner, identifying related classes and taxonomies.

  12. Object Recognition in Flight: How Do Bees Distinguish between 3D Shapes?

    PubMed

    Werner, Annette; Stürzl, Wolfgang; Zanker, Johannes

    2016-01-01

    Honeybees (Apis mellifera) discriminate multiple object features such as colour, pattern and 2D shape, but it remains unknown whether and how bees recover three-dimensional shape. Here we show that bees can recognize objects by their three-dimensional form, whereby they employ an active strategy to uncover the depth profiles. We trained individual, free flying honeybees to collect sugar water from small three-dimensional objects made of styrofoam (sphere, cylinder, cuboids) or folded paper (convex, concave, planar) and found that bees can easily discriminate between these stimuli. We also tested possible strategies employed by the bees to uncover the depth profiles. For the card stimuli, we excluded overall shape and pictorial features (shading, texture gradients) as cues for discrimination. Lacking sufficient stereo vision, bees are known to use speed gradients in optic flow to detect edges; could the bees apply this strategy also to recover the fine details of a surface depth profile? Analysing the bees' flight tracks in front of the stimuli revealed specific combinations of flight maneuvers (lateral translations in combination with yaw rotations), which are particularly suitable to extract depth cues from motion parallax. We modelled the generated optic flow and found characteristic patterns of angular displacement corresponding to the depth profiles of our stimuli: optic flow patterns from pure translations successfully recovered depth relations from the magnitude of angular displacements, additional rotation provided robust depth information based on the direction of the displacements; thus, the bees flight maneuvers may reflect an optimized visuo-motor strategy to extract depth structure from motion signals. The robustness and simplicity of this strategy offers an efficient solution for 3D-object-recognition without stereo vision, and could be employed by other flying insects, or mobile robots. PMID:26886006

  13. Object Recognition in Flight: How Do Bees Distinguish between 3D Shapes?

    PubMed

    Werner, Annette; Stürzl, Wolfgang; Zanker, Johannes

    2016-01-01

    Honeybees (Apis mellifera) discriminate multiple object features such as colour, pattern and 2D shape, but it remains unknown whether and how bees recover three-dimensional shape. Here we show that bees can recognize objects by their three-dimensional form, whereby they employ an active strategy to uncover the depth profiles. We trained individual, free flying honeybees to collect sugar water from small three-dimensional objects made of styrofoam (sphere, cylinder, cuboids) or folded paper (convex, concave, planar) and found that bees can easily discriminate between these stimuli. We also tested possible strategies employed by the bees to uncover the depth profiles. For the card stimuli, we excluded overall shape and pictorial features (shading, texture gradients) as cues for discrimination. Lacking sufficient stereo vision, bees are known to use speed gradients in optic flow to detect edges; could the bees apply this strategy also to recover the fine details of a surface depth profile? Analysing the bees' flight tracks in front of the stimuli revealed specific combinations of flight maneuvers (lateral translations in combination with yaw rotations), which are particularly suitable to extract depth cues from motion parallax. We modelled the generated optic flow and found characteristic patterns of angular displacement corresponding to the depth profiles of our stimuli: optic flow patterns from pure translations successfully recovered depth relations from the magnitude of angular displacements, additional rotation provided robust depth information based on the direction of the displacements; thus, the bees flight maneuvers may reflect an optimized visuo-motor strategy to extract depth structure from motion signals. The robustness and simplicity of this strategy offers an efficient solution for 3D-object-recognition without stereo vision, and could be employed by other flying insects, or mobile robots.

  14. Three-dimensional object recognition using gradient descent and the universal 3-D array grammar

    NASA Astrophysics Data System (ADS)

    Baird, Leemon C., III; Wang, Patrick S. P.

    1992-02-01

    A new algorithm is presented for applying Marill's minimum standard deviation of angles (MSDA) principle for interpreting line drawings without models. Even though no explicit models or additional heuristics are included, the algorithm tends to reach the same 3-D interpretations of 2-D line drawings that humans do. Marill's original algorithm repeatedly generated a set of interpretations and chose the one with the lowest standard deviation of angles (SDA). The algorithm presented here explicitly calculates the partial derivatives of SDA with respect to all adjustable parameters, and follows this gradient to minimize SDA. For a picture with lines meeting at m points forming n angles, the gradient descent algorithm requires O(n) time to adjust all the points, while the original algorithm required O(mn) time to do so. For the pictures described by Marill, this gradient descent algorithm running on a Macintosh II was found to be one to two orders of magnitude faster than the original algorithm running on a Symbolics, while still giving comparable results. Once the 3-D interpretation of the line drawing has been found, the 3-D object can be reduced to a description string using the Universal 3-D Array Grammar. This is a general grammar which allows any connected object represented as a 3-D array of pixels to be reduced to a description string. The algorithm based on this grammar is well suited to parallel computation, and could run efficiently on parallel hardware. This paper describes both the MSDA gradient descent algorithm and the Universal 3-D Array Grammar algorithm. Together, they transform a 2-D line drawing represented as a list of line segments into a string describing the 3-D object pictured. The strings could then be used for object recognition, learning, or storage for later manipulation.

  15. Object Recognition in Flight: How Do Bees Distinguish between 3D Shapes?

    PubMed Central

    Werner, Annette; Stürzl, Wolfgang; Zanker, Johannes

    2016-01-01

    Honeybees (Apis mellifera) discriminate multiple object features such as colour, pattern and 2D shape, but it remains unknown whether and how bees recover three-dimensional shape. Here we show that bees can recognize objects by their three-dimensional form, whereby they employ an active strategy to uncover the depth profiles. We trained individual, free flying honeybees to collect sugar water from small three-dimensional objects made of styrofoam (sphere, cylinder, cuboids) or folded paper (convex, concave, planar) and found that bees can easily discriminate between these stimuli. We also tested possible strategies employed by the bees to uncover the depth profiles. For the card stimuli, we excluded overall shape and pictorial features (shading, texture gradients) as cues for discrimination. Lacking sufficient stereo vision, bees are known to use speed gradients in optic flow to detect edges; could the bees apply this strategy also to recover the fine details of a surface depth profile? Analysing the bees’ flight tracks in front of the stimuli revealed specific combinations of flight maneuvers (lateral translations in combination with yaw rotations), which are particularly suitable to extract depth cues from motion parallax. We modelled the generated optic flow and found characteristic patterns of angular displacement corresponding to the depth profiles of our stimuli: optic flow patterns from pure translations successfully recovered depth relations from the magnitude of angular displacements, additional rotation provided robust depth information based on the direction of the displacements; thus, the bees flight maneuvers may reflect an optimized visuo-motor strategy to extract depth structure from motion signals. The robustness and simplicity of this strategy offers an efficient solution for 3D-object-recognition without stereo vision, and could be employed by other flying insects, or mobile robots. PMID:26886006

  16. A novel margin-based linear embedding technique for visual object recognition

    NASA Astrophysics Data System (ADS)

    Dornaika, F.; Assoum, A.

    2012-01-01

    Linear Dimensionality Reduction (LDR) techniques have been increasingly important in computer vision and pattern recognition since they permit a relatively simple mapping of data onto a lower dimensional subspace, leading to simple and computationally efficient classification strategies. Recently, many linear discriminant methods have been developed in order to reduce the dimensionality of visual data and to enhance the discrimination between different groups or classes. Although many linear discriminant analysis methods have been proposed in the literature, they suffer from at least one of the following shortcomings: i) they require the setting of many parameters (e.g., the neighborhood sizes for homogeneous and heterogeneous samples), ii) they suffer from the Small Sample Size problem that often occurs when dealing with visual data sets for which the number of samples is less than the dimension of the sample, and iii) most of the traditional subspace learning methods have to determine the dimension of the projected space by either cross-validation or exhaustive search. In this paper, we propose a novel margin-based linear embedding method that exploits the nearest hit and the nearest miss samples only. Our proposed method tackles all the above shortcomings. It finds the projection directions such that the sum of local margins is maximized. Our proposed approach has been applied to the problem of appearancebased face recognition. Experimental results performed on four public face databases show that the proposed approach can give better generalization performance than the competing methods. These competing methods used for performance comparison were: Principal Component Analysis (PCA), Locality Preserving Projections (LPP), Average Neighborhood Margin Maximization (ANMM), and Maximally Collapsing Metric Learning algorithm (MCML). The proposed approach could also be applied to other category of objects characterized by large variations in their appearance.

  17. Crowded and sparse domains in object recognition: consequences for categorization and naming.

    PubMed

    Gale, Tim M; Laws, Keith R; Foley, Kerry

    2006-03-01

    Some models of object recognition propose that items from structurally crowded categories (e.g., living things) permit faster access to superordinate semantic information than structurally dissimilar categories (e.g., nonliving things), but slower access to individual object information when naming items. We present four experiments that utilize the same matched stimuli: two examine superordinate categorization and two examine picture naming. Experiments 1 and 2 required participants to sort pictures into their appropriate superordinate categories and both revealed faster categorization for living than nonliving things. Nonetheless, the living thing superiority disappeared when the atypical categories of body parts and musical instruments were excluded. Experiment 3 examined naming latency and found no difference between living and nonliving things. This finding was replicated in Experiment 4 where the same items were presented in different formats (e.g., color and line-drawn versions). Taken as a whole, these experiments show that the ease with which people categorize items maps strongly onto the ease with which they name them. PMID:16377049

  18. Deramciclane improves object recognition in rats: potential role of NMDA receptors.

    PubMed

    Kertész, Szabolcs; Kapus, Gábor; Gacsályi, István; Lévay, György

    2010-02-01

    The cognition-enhancing properties of deramciclane (N,N-dimethyl-2-([(1R,4R,6S)-1,7,7-trimethyl-6-phenyl-6-bicyclo[2.2.1]heptanyl]oxy)ethanamine) and memantine (3,5-dimethyl-tricyclo[3.3.1.1(3,7)]decylamine-3,5-dimethyladamantan-1-amine) were evaluated in the novel object recognition (OR) test in the rat, while their effect in comparison with other N-methyl-D-aspartate (NMDA) receptor blockers such us MK-801 ([+]-5-methyl-10,11-dihydro-5H-dibenzocyclohepten-5,10-imine maleate) and CPP ([+/-]-3-(2-carboxypiperazin-4-yl)propyl-1-phosphonic acid) on NMDA-evoked spreading depression (SD) was investigated in the chicken retina, in vitro. In the OR test, pretreatment of rats with either deramciclane (30 mg/kg p.o.) or memantine (10 and 30 mg/kg, p.o.) resulted in preference for the novel object, compared to the familiar one, indicating procognitive activity of the compounds. In the in vitro studies memantine (10-30 M), or deramciclane (30-100 M) as well as CPP (0.1-1 M), MK-801 (0.3-1 M), concentration-dependently inhibited NMDA evoked SD. Furthermore, the inhibitory effect of memantine, deramciclane and MK-801 was activity-dependent. These results support the role of NMDA receptors in the procognitive effect of deramciclane.

  19. A method of 3D object recognition and localization in a cloud of points

    NASA Astrophysics Data System (ADS)

    Bielicki, Jerzy; Sitnik, Robert

    2013-12-01

    The proposed method given in this article is prepared for analysis of data in the form of cloud of points directly from 3D measurements. It is designed for use in the end-user applications that can directly be integrated with 3D scanning software. The method utilizes locally calculated feature vectors (FVs) in point cloud data. Recognition is based on comparison of the analyzed scene with reference object library. A global descriptor in the form of a set of spatially distributed FVs is created for each reference model. During the detection process, correlation of subsets of reference FVs with FVs calculated in the scene is computed. Features utilized in the algorithm are based on parameters, which qualitatively estimate mean and Gaussian curvatures. Replacement of differentiation with averaging in the curvatures estimation makes the algorithm more resistant to discontinuities and poor quality of the input data. Utilization of the FV subsets allows to detect partially occluded and cluttered objects in the scene, while additional spatial information maintains false positive rate at a reasonably low level.

  20. Spatial Memory and Long-Term Object Recognition Are Impaired by Circadian Arrhythmia and Restored by the GABAAAntagonist Pentylenetetrazole

    PubMed Central

    Ruby, Norman F.; Fernandez, Fabian; Garrett, Alex; Klima, Jessy; Zhang, Pei; Sapolsky, Robert; Heller, H. Craig

    2013-01-01

    Performance on many memory tests varies across the day and is severely impaired by disruptions in circadian timing. We developed a noninvasive method to permanently eliminate circadian rhythms in Siberian hamsters (Phodopussungorus) so that we could investigate the contribution of the circadian system to learning and memory in animals that are neurologically and genetically intact. Male and female adult hamsters were rendered arrhythmic by a disruptive phase shift protocol that eliminates cycling of clock genes within the suprachiasmatic nucleus (SCN), but preserves sleep architecture. These arrhythmic animals have deficits in spatial working memory and in long-term object recognition memory. In a T-maze, rhythmic control hamsters exhibited spontaneous alternation behavior late in the day and at night, but made random arm choices early in the day. By contrast, arrhythmic animals made only random arm choices at all time points. Control animals readily discriminated novel objects from familiar ones, whereas arrhythmic hamsters could not. Since the SCN is primarily a GABAergic nucleus, we hypothesized that an arrhythmic SCN could interfere with memory by increasing inhibition in hippocampal circuits. To evaluate this possibility, we administered the GABAA antagonist pentylenetetrazole (PTZ; 0.3 or 1.0 mg/kg/day) to arrhythmic hamsters for 10 days, which is a regimen previously shown to produce long-term improvements in hippocampal physiology and behavior in Ts65Dn (Down syndrome) mice. PTZ restored long-term object recognition and spatial working memory for at least 30 days after drug treatment without restoring circadian rhythms. PTZ did not augment memory in control (entrained) animals, but did increase their activity during the memory tests. Our findings support the hypothesis that circadian arrhythmia impairs declarative memory by increasing the relative influence of GABAergic inhibition in the hippocampus. PMID:24009680

  1. Spatial memory and long-term object recognition are impaired by circadian arrhythmia and restored by the GABAAAntagonist pentylenetetrazole.

    PubMed

    Ruby, Norman F; Fernandez, Fabian; Garrett, Alex; Klima, Jessy; Zhang, Pei; Sapolsky, Robert; Heller, H Craig

    2013-01-01

    Performance on many memory tests varies across the day and is severely impaired by disruptions in circadian timing. We developed a noninvasive method to permanently eliminate circadian rhythms in Siberian hamsters (Phodopus sungorus) [corrected] so that we could investigate the contribution of the circadian system to learning and memory in animals that are neurologically and genetically intact. Male and female adult hamsters were rendered arrhythmic by a disruptive phase shift protocol that eliminates cycling of clock genes within the suprachiasmatic nucleus (SCN), but preserves sleep architecture. These arrhythmic animals have deficits in spatial working memory and in long-term object recognition memory. In a T-maze, rhythmic control hamsters exhibited spontaneous alternation behavior late in the day and at night, but made random arm choices early in the day. By contrast, arrhythmic animals made only random arm choices at all time points. Control animals readily discriminated novel objects from familiar ones, whereas arrhythmic hamsters could not. Since the SCN is primarily a GABAergic nucleus, we hypothesized that an arrhythmic SCN could interfere with memory by increasing inhibition in hippocampal circuits. To evaluate this possibility, we administered the GABAA antagonist pentylenetetrazole (PTZ; 0.3 or 1.0 mg/kg/day) to arrhythmic hamsters for 10 days, which is a regimen previously shown to produce long-term improvements in hippocampal physiology and behavior in Ts65Dn (Down syndrome) mice. PTZ restored long-term object recognition and spatial working memory for at least 30 days after drug treatment without restoring circadian rhythms. PTZ did not augment memory in control (entrained) animals, but did increase their activity during the memory tests. Our findings support the hypothesis that circadian arrhythmia impairs declarative memory by increasing the relative influence of GABAergic inhibition in the hippocampus.

  2. Automatic TLI recognition system. Part 2: User`s guide

    SciTech Connect

    Partin, J.K.; Lassahn, G.D.; Davidson, J.R.

    1994-05-01

    This report describes an automatic target recognition system for fast screening of large amounts of multi-sensor image data, based on low-cost parallel processors. This system uses image data fusion and gives uncertainty estimates. It is relatively low cost, compact, and transportable. The software is easily enhanced to expand the system`s capabilities, and the hardware is easily expandable to increase the system`s speed. This volume is a user`s manual for an Automatic Target Recognition (ATR) system. This guide is intended to provide enough information and instruction to allow individuals to the system for their own applications.

  3. Differential Roles for "Nr4a1" and "Nr4a2" in Object Location vs. Object Recognition Long-Term Memory

    ERIC Educational Resources Information Center

    McNulty, Susan E.; Barrett, Ruth M.; Vogel-Ciernia, Annie; Malvaez, Melissa; Hernandez, Nicole; Davatolhagh, M. Felicia; Matheos, Dina P.; Schiffman, Aaron; Wood, Marcelo A.

    2012-01-01

    "Nr4a1" and "Nr4a2" are transcription factors and immediate early genes belonging to the nuclear receptor Nr4a family. In this study, we examine their role in long-term memory formation for object location and object recognition. Using siRNA to block expression of either "Nr4a1" or "Nr4a2", we found that "Nr4a2" is necessary for both long-term…

  4. System for controlled acoustic rotation of objects

    NASA Technical Reports Server (NTRS)

    Barmatz, M. B. (Inventor)

    1983-01-01

    A system is described for use with acoustically levitated objects, which enables close control of rotation of the object. One system includes transducers that propagate acoustic waves along the three dimensions (X, Y, Z) of a chamber of rectangular cross section. Each transducers generates one wave which is resonant to a corresponding chamber dimension to acoustically levitate an object, and additional higher frequency resonant wavelengths for controlling rotation of the object. The three chamber dimensions and the corresponding three levitation modes (resonant wavelengths) are all different, to avoid degeneracy, or interference, of waves with one another, that could have an effect on object rotation. Only the higher frequencies, with pairs of them having the same wavelength, are utilized to control rotation, so that rotation is controlled independently of levitation and about any arbitrarily chosen axis.

  5. System for controlled acoustic rotation of objects

    NASA Astrophysics Data System (ADS)

    Barmatz, M. B.

    1983-07-01

    A system is described for use with acoustically levitated objects, which enables close control of rotation of the object. One system includes transducers that propagate acoustic waves along the three dimensions (X, Y, Z) of a chamber of rectangular cross section. Each transducers generates one wave which is resonant to a corresponding chamber dimension to acoustically levitate an object, and additional higher frequency resonant wavelengths for controlling rotation of the object. The three chamber dimensions and the corresponding three levitation modes (resonant wavelengths) are all different, to avoid degeneracy, or interference, of waves with one another, that could have an effect on object rotation. Only the higher frequencies, with pairs of them having the same wavelength, are utilized to control rotation, so that rotation is controlled independently of levitation and about any arbitrarily chosen axis.

  6. Social context predicts recognition systems in ant queens.

    PubMed

    Dreier, S; D'Ettorre, P

    2009-03-01

    Recognition of group-members is a key feature of sociality. Ants use chemical communication to discriminate nestmates from intruders, enhancing kin cooperation and preventing parasitism. The recognition code is embedded in their cuticular chemical profile, which typically varies between colonies. We predicted that ants might be capable of accurate recognition in unusual situations when few individuals interact repeatedly, as new colonies started by two to three queens. Individual recognition would be favoured by selection when queens establish dominance hierarchies, because repeated fights for dominance are costly; but it would not evolve in absence of hierarchies. We previously showed that Pachycondyla co-founding queens, which form dominance hierarchies, have accurate individual recognition based on chemical cues. Here, we used the ant Lasius niger to test the null hypothesis that individual recognition does not occur when co-founding queens do not establish dominance hierarchies. Indeed, L. niger queens show a similar level of aggression towards both co-foundresses and intruders, indicating that they are unable of individual recognition, contrary to Pachycondyla. Additionally, the variation in chemical profiles of Lasius and Pachycondyla queens is comparable, thus informational constraints are unlikely to apply. We conclude that selection pressure from the social context is of crucial significance for the sophistication of recognition systems.

  7. Covert recognition and the neural system for face processing.

    PubMed

    Schweinberger, Stefan R; Burton, A Mike

    2003-02-01

    In this viewpoint, we discuss the new evidence on covert face recognition in prosopagnosia presented by Bobes et al. (2003, this issue) and by Sperber and Spinnler (2003, this issue). Contrary to earlier hypotheses, both papers agree that covert and overt face recognition are based on the same mechanism. In line with this suggestion, an analysis of reported cases with prosopagnosia indicates that a degree of successful encoding of facial representations is a prerequisite for covert recognition to occur. While we agree with this general conclusion as far as Bobes et al.'s and Sperber and Spinnler's data are concerned, we also discuss evidence for a dissociation between different measures of covert recognition. Specifically, studies in patients with Capgras delusion and patients with prosopagnosia suggest that skin conductance and behavioural indexes of covert face recognition are mediated by partially different mechanisms. We also discuss implications of the new data for models of normal face recognition that have been successful in simulating covert recognition phenomena (e.g., Young and Burton, 1999, and O'Reilly et al., 1999). Finally, in reviewing recent neurophysiological and brain imaging evidence concerning the neural system for face processing, we argue that the relationship between ERP components (specifically, N170, N250r, and N400) and different cognitive processes in face recognition is beginning to emerge. PMID:12627750

  8. Object-Oriented Systems for Information Management.

    ERIC Educational Resources Information Center

    Jeffcoate, Judith

    1996-01-01

    Describes the use of object technology for the development of information management systems. Notes the benefits of modelling complex real-world systems and the increases in productivity leading to flexible, reusable, and maintainable software. Discusses problems, support for multimedia data types, and storage capabilities. (AEF)

  9. Brain dynamics of upstream perceptual processes leading to visual object recognition: a high density ERP topographic mapping study.

    PubMed

    Schettino, Antonio; Loeys, Tom; Delplanque, Sylvain; Pourtois, Gilles

    2011-04-01

    Recent studies suggest that visual object recognition is a proactive process through which perceptual evidence accumulates over time before a decision can be made about the object. However, the exact electrophysiological correlates and time-course of this complex process remain unclear. In addition, the potential influence of emotion on this process has not been investigated yet. We recorded high density EEG in healthy adult participants performing a novel perceptual recognition task. For each trial, an initial blurred visual scene was first shown, before the actual content of the stimulus was gradually revealed by progressively adding diagnostic high spatial frequency information. Participants were asked to stop this stimulus sequence as soon as they could correctly perform an animacy judgment task. Behavioral results showed that participants reliably gathered perceptual evidence before recognition. Furthermore, prolonged exploration times were observed for pleasant, relative to either neutral or unpleasant scenes. ERP results showed distinct effects starting at 280 ms post-stimulus onset in distant brain regions during stimulus processing, mainly characterized by: (i) a monotonic accumulation of evidence, involving regions of the posterior cingulate cortex/parahippocampal gyrus, and (ii) true categorical recognition effects in medial frontal regions, including the dorsal anterior cingulate cortex. These findings provide evidence for the early involvement, following stimulus onset, of non-overlapping brain networks during proactive processes eventually leading to visual object recognition.

  10. Brain dynamics of upstream perceptual processes leading to visual object recognition: a high density ERP topographic mapping study.

    PubMed

    Schettino, Antonio; Loeys, Tom; Delplanque, Sylvain; Pourtois, Gilles

    2011-04-01

    Recent studies suggest that visual object recognition is a proactive process through which perceptual evidence accumulates over time before a decision can be made about the object. However, the exact electrophysiological correlates and time-course of this complex process remain unclear. In addition, the potential influence of emotion on this process has not been investigated yet. We recorded high density EEG in healthy adult participants performing a novel perceptual recognition task. For each trial, an initial blurred visual scene was first shown, before the actual content of the stimulus was gradually revealed by progressively adding diagnostic high spatial frequency information. Participants were asked to stop this stimulus sequence as soon as they could correctly perform an animacy judgment task. Behavioral results showed that participants reliably gathered perceptual evidence before recognition. Furthermore, prolonged exploration times were observed for pleasant, relative to either neutral or unpleasant scenes. ERP results showed distinct effects starting at 280 ms post-stimulus onset in distant brain regions during stimulus processing, mainly characterized by: (i) a monotonic accumulation of evidence, involving regions of the posterior cingulate cortex/parahippocampal gyrus, and (ii) true categorical recognition effects in medial frontal regions, including the dorsal anterior cingulate cortex. These findings provide evidence for the early involvement, following stimulus onset, of non-overlapping brain networks during proactive processes eventually leading to visual object recognition. PMID:21237274

  11. CANTAB object recognition and language tests to detect aging cognitive decline: an exploratory comparative study

    PubMed Central

    Cabral Soares, Fernanda; de Oliveira, Thaís Cristina Galdino; de Macedo, Liliane Dias e Dias; Tomás, Alessandra Mendonça; Picanço-Diniz, Domingos Luiz Wanderley; Bento-Torres, João; Bento-Torres, Natáli Valim Oliver; Picanço-Diniz, Cristovam Wanderley

    2015-01-01

    Objective The recognition of the limits between normal and pathological aging is essential to start preventive actions. The aim of this paper is to compare the Cambridge Neuropsychological Test Automated Battery (CANTAB) and language tests to distinguish subtle differences in cognitive performances in two different age groups, namely young adults and elderly cognitively normal subjects. Method We selected 29 young adults (29.9±1.06 years) and 31 older adults (74.1±1.15 years) matched by educational level (years of schooling). All subjects underwent a general assessment and a battery of neuropsychological tests, including the Mini Mental State Examination, visuospatial learning, and memory tasks from CANTAB and language tests. Cluster and discriminant analysis were applied to all neuropsychological test results to distinguish possible subgroups inside each age group. Results Significant differences in the performance of aged and young adults were detected in both language and visuospatial memory tests. Intragroup cluster and discriminant analysis revealed that CANTAB, as compared to language tests, was able to detect subtle but significant differences between the subjects. Conclusion Based on these findings, we concluded that, as compared to language tests, large-scale application of automated visuospatial tests to assess learning and memory might increase our ability to discern the limits between normal and pathological aging. PMID:25565785

  12. α7nAchR/NMDAR coupling affects NMDAR function and object recognition.

    PubMed

    Li, Shupeng; Nai, Qiang; Lipina, Tatiana V; Roder, John C; Liu, Fang

    2013-12-20

    The α7 nicotinic acetylcholine receptor (nAchR) and NMDA glutamate receptor (NMDAR) are both ligand-gated ion channels permeable to Ca2+ and Na+. Previous studies have demonstrated functional modulation of NMDARs by nAchRs, although the molecular mechanism remains largely unknown. We have previously reported that α7nAchR forms a protein complex with the NMDAR through a protein-protein interaction. We also developed an interfering peptide that is able to disrupt the α7nAchR-NMDAR complex and blocks cue-induced reinstatement of nicotine-seeking in rat models of relapse. In the present study, we investigated whether the α7nAchR-NMDAR interaction is responsible for the functional modulation of NMDAR by α7nAchR using both electrophysiological and behavioral tests. We have found that activation of α7nAchR upregulates NMDAR-mediated whole cell currents and LTP of mEPSC in cultured hippocampal neurons, which can be abolished by the interfering peptide that disrupts the α7nAchR-NMDAR interaction. Moreover, administration of the interfering peptide in mice impairs novel object recognition but not Morris water maze performance. Our results suggest that α7nAchR/NMDAR coupling may selectively affect some aspects of learning and memory.

  13. Dityrosine administration induces novel object recognition deficits in young adulthood mice.

    PubMed

    Ran, Yumei; Yan, Biao; Li, Zhuqing; Ding, Yinyi; Shi, Yonghui; Le, Guowei

    2016-10-01

    Dietary modifications have been shown to contribute to the physical and mental diseases. Oxidative modifications of protein can be easily found in protein-rich food such as meat and milk products. Previous studies mainly focus on the consequences of lipid oxidation products intake in vivo, but the effects of protein oxidation products consumption have been largely neglected. Oxidants have been shown to play an important role in aging and neurodegenerative diseases. Dityrosine is the oxidated product of tyrosine residues in protein which is considered as a biomarker for oxidative stress, but the potential deleterious effects of dityrosine are unknown. In the present study, we explored the effects of dityrosine administration on the behavioral aspect. We found that dityrosine-ingested mice displayed impaired memory during novel object recognition test, but no influence to the spatial memory in Morris water maze compared with the saline group. Other aspects of neurobehavioral function such as locomotor activity, anxiety and social behavior were not affected by dityrosine ingestion. Furthermore, we found that dityrosine-ingested mice showed decreased expression level of NMDA receptor subunits Nr1, Nr2a, Nr2b as well as Bdnf, Trkb. Our study suggests that dityrosine exposure impairs hippocampus-dependent nonspatial memory accompanied by modulation of NMDA receptor subunits and Bdnf expression. PMID:27317839

  14. Object Occlusion Detection Using Automatic Camera Calibration for a Wide-Area Video Surveillance System.

    PubMed

    Jung, Jaehoon; Yoon, Inhye; Paik, Joonki

    2016-06-25

    This paper presents an object occlusion detection algorithm using object depth information that is estimated by automatic camera calibration. The object occlusion problem is a major factor to degrade the performance of object tracking and recognition. To detect an object occlusion, the proposed algorithm consists of three steps: (i) automatic camera calibration using both moving objects and a background structure; (ii) object depth estimation; and (iii) detection of occluded regions. The proposed algorithm estimates the depth of the object without extra sensors but with a generic red, green and blue (RGB) camera. As a result, the proposed algorithm can be applied to improve the performance of object tracking and object recognition algorithms for video surveillance systems.

  15. Object Occlusion Detection Using Automatic Camera Calibration for a Wide-Area Video Surveillance System

    PubMed Central

    Jung, Jaehoon; Yoon, Inhye; Paik, Joonki

    2016-01-01

    This paper presents an object occlusion detection algorithm using object depth information that is estimated by automatic camera calibration. The object occlusion problem is a major factor to degrade the performance of object tracking and recognition. To detect an object occlusion, the proposed algorithm consists of three steps: (i) automatic camera calibration using both moving objects and a background structure; (ii) object depth estimation; and (iii) detection of occluded regions. The proposed algorithm estimates the depth of the object without extra sensors but with a generic red, green and blue (RGB) camera. As a result, the proposed algorithm can be applied to improve the performance of object tracking and object recognition algorithms for video surveillance systems. PMID:27347978

  16. Object Occlusion Detection Using Automatic Camera Calibration for a Wide-Area Video Surveillance System.

    PubMed

    Jung, Jaehoon; Yoon, Inhye; Paik, Joonki

    2016-01-01

    This paper presents an object occlusion detection algorithm using object depth information that is estimated by automatic camera calibration. The object occlusion problem is a major factor to degrade the performance of object tracking and recognition. To detect an object occlusion, the proposed algorithm consists of three steps: (i) automatic camera calibration using both moving objects and a background structure; (ii) object depth estimation; and (iii) detection of occluded regions. The proposed algorithm estimates the depth of the object without extra sensors but with a generic red, green and blue (RGB) camera. As a result, the proposed algorithm can be applied to improve the performance of object tracking and object recognition algorithms for video surveillance systems. PMID:27347978

  17. Contrasting the edge- and surface-based theories of object recognition: behavioral evidence from macaques (Macaca mulatta).

    PubMed

    Parron, Carole; Washburn, David

    2010-01-01

    This study assessed the contribution of edge and surface cues on object representation in macaques (Macaca mulatta). In Experiments 1 and 2, 5 macaques were trained to discriminate 4 simple volumetric objects (geons) and were subsequently tested for their ability to recognize line drawings, silhouettes, and light changes of these geons. Performance was above chance in all test conditions and was similarly high for the line drawings and silhouettes of geons, suggesting the use of the outline shape to recognize the original objects. In addition, transfer for the geons seen under new lighting was greater than for the other stimuli, stressing the importance of the shading information. Experiment 3, using geons filled with new textures, showed that a radical change in the surface cues does not prevent object recognition. It is concluded that these findings support a surface-based theory of object recognition in macaques, although it does not exclude the contribution of edge cues, especially when surface details are not available.

  18. Feature binding in perceptual priming and in episodic object recognition: evidence from event-related brain potentials.

    PubMed

    Groh-Bordin, Christian; Zimmer, Hubert D; Mecklinger, Axel

    2005-08-01

    It is argued that explicit remembering is based on so-called episodic tokens binding together all perceptual features of a visual object. In episodic recognition, these features are collectively reactivated. In support of this view, it has been shown that changing sensory features of a stimulus from study to test decreases subject's performance in an episodic recognition task, even though the changed features are irrelevant for the recognition judgment. On the other hand, repetition priming is unaffected by such manipulations of perceptual specificity. Implicit memory performance is therefore thought to depend on structural representations, so-called types, comprising only invariant perceptual features, but no exemplar-specific details. Event-related potentials (ERPs) in our study revealed electrophysiological evidence for the differential involvement of these perceptual memory traces in explicit and implicit memory tasks. Participants attended either a living-nonliving task or an episodic recognition task with visually presented objects. During test both groups of participants processed new objects and old objects, which were repeated either identically or in a mirror-reversed version. In the implicit task ERPs showed an occipitoparietal repetition effect, which was the same for identically repeated items and mirror reversals. In contrast, in the explicit task an early mid-frontal old/new effect for identical but not for mirror-reversed old objects was observed indicating involuntary access to perceptual information during episodic retrieval. A later portion of the old/new effect solely differentiated both types of old items from new ones. PMID:16099366

  19. Object-oriented Geographic Information System Framework

    SciTech Connect

    Lurie, Gordon

    2003-03-01

    JeoViewer is an intelligent object-oriented geographic information system (GIS) framework written in Java that provides transparent linkage to any object’s data, behaviors, and optimized spatial geometry representation. Tools are provided for typical GIS functionality, data ingestion, data export, and integration with other frameworks. The primary difference between Jeo Viewer and traditional GIS systems is that traditional GIS systems offer static views of geo-spatial data while JeoViewer can be dynamically coupled to models and live data streams which dynamically change the state of the object which can be immediately represented in JeoViewer. Additionally, JeoViewer’s object-oriented paradigm provides a more natural representation of spatial data. A rich layer hierarchy allows arbitrary grouping of objects based on any relationship as well as the traditional GIS vertical ordering of objects. JeoViewer can run as a standalone product, extended with additional analysis functionality, or embedded in another framework.

  20. Object-oriented Geographic Information System Framework

    2003-03-01

    JeoViewer is an intelligent object-oriented geographic information system (GIS) framework written in Java that provides transparent linkage to any object’s data, behaviors, and optimized spatial geometry representation. Tools are provided for typical GIS functionality, data ingestion, data export, and integration with other frameworks. The primary difference between Jeo Viewer and traditional GIS systems is that traditional GIS systems offer static views of geo-spatial data while JeoViewer can be dynamically coupled to models and live datamore » streams which dynamically change the state of the object which can be immediately represented in JeoViewer. Additionally, JeoViewer’s object-oriented paradigm provides a more natural representation of spatial data. A rich layer hierarchy allows arbitrary grouping of objects based on any relationship as well as the traditional GIS vertical ordering of objects. JeoViewer can run as a standalone product, extended with additional analysis functionality, or embedded in another framework.« less

  1. Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence

    PubMed Central

    Cichy, Radoslaw Martin; Khosla, Aditya; Pantazis, Dimitrios; Torralba, Antonio; Oliva, Aude

    2016-01-01

    The complex multi-stage architecture of cortical visual pathways provides the neural basis for efficient visual object recognition in humans. However, the stage-wise computations therein remain poorly understood. Here, we compared temporal (magnetoencephalography) and spatial (functional MRI) visual brain representations with representations in an artificial deep neural network (DNN) tuned to the statistics of real-world visual recognition. We showed that the DNN captured the stages of human visual processing in both time and space from early visual areas towards the dorsal and ventral streams. Further investigation of crucial DNN parameters revealed that while model architecture was important, training on real-world categorization was necessary to enforce spatio-temporal hierarchical relationships with the brain. Together our results provide an algorithmically informed view on the spatio-temporal dynamics of visual object recognition in the human visual brain. PMID:27282108

  2. Reduction of Subjective and Objective System Complexity

    NASA Technical Reports Server (NTRS)

    Watson, Michael D.

    2015-01-01

    Occam's razor is often used in science to define the minimum criteria to establish a physical or philosophical idea or relationship. Albert Einstein is attributed the saying "everything should be made as simple as possible, but not simpler". These heuristic ideas are based on a belief that there is a minimum state or set of states for a given system or phenomena. In looking at system complexity, these heuristics point us to an idea that complexity can be reduced to a minimum. How then, do we approach a reduction in complexity? Complexity has been described as a subjective concept and an objective measure of a system. Subjective complexity is based on human cognitive comprehension of the functions and inter relationships of a system. Subjective complexity is defined by the ability to fully comprehend the system. Simplifying complexity, in a subjective sense, is thus gaining a deeper understanding of the system. As Apple's Jonathon Ive has stated," It's not just minimalism or the absence of clutter. It involves digging through the depth of complexity. To be truly simple, you have to go really deep". Simplicity is not the absence of complexity but a deeper understanding of complexity. Subjective complexity, based on this human comprehension, cannot then be discerned from the sociological concept of ignorance. The inability to comprehend a system can be either a lack of knowledge, an inability to understand the intricacies of a system, or both. Reduction in this sense is based purely on a cognitive ability to understand the system and no system then may be truly complex. From this view, education and experience seem to be the keys to reduction or eliminating complexity. Objective complexity, is the measure of the systems functions and interrelationships which exist independent of human comprehension. Jonathon Ive's statement does not say that complexity is removed, only that the complexity is understood. From this standpoint, reduction of complexity can be approached

  3. A hybrid recognition system for off-line handwritten characters.

    PubMed

    Katiyar, Gauri; Mehfuz, Shabana

    2016-01-01

    Computer based pattern recognition is a process that involves several sub-processes, including pre-processing, feature extraction, feature selection, and classification. Feature extraction is the estimation of certain attributes of the target patterns. Selection of the right set of features is the most crucial and complex part of building a pattern recognition system. In this work we have combined multiple features extracted using seven different approaches. The novelty of this approach is to achieve better accuracy and reduced computational time for recognition of handwritten characters using Genetic Algorithm which optimizes the number of features along with a simple and adaptive Multi Layer Perceptron classifier. Experiments have been performed using standard database of CEDAR (Centre of Excellence for Document Analysis and Recognition) for English alphabet. The experimental results obtained on this database demonstrate the effectiveness of this system. PMID:27066370

  4. Invariant Visual Object and Face Recognition: Neural and Computational Bases, and a Model, VisNet

    PubMed Central

    Rolls, Edmund T.

    2012-01-01

    Neurophysiological evidence for invariant representations of objects and faces in the primate inferior temporal visual cortex is described. Then a computational approach to how invariant representations are formed in the brain is described that builds on the neurophysiology. A feature hierarchy model in which invariant representations can be built by self-organizing learning based on the temporal and spatial statistics of the visual input produced by objects as they transform in the world is described. VisNet can use temporal continuity in an associative synaptic learning rule with a short-term memory trace, and/or it can use spatial continuity in continuous spatial transformation learning which does not require a temporal trace. The model of visual processing in the ventral cortical stream can build representations of objects that are invariant with respect to translation, view, size, and also lighting. The model has been extended to provide an account of invariant representations in the dorsal visual system of the global motion produced by objects such as looming, rotation, and object-based movement. The model has been extended to incorporate top-down feedback connections to model the control of attention by biased competition in, for example, spatial and object search tasks. The approach has also been extended to account for how the visual system can select single objects in complex visual scenes, and how multiple objects can be represented in a scene. The approach has also been extended to provide, with an additional layer, for the development of representations of spatial scenes of the type found in the hippocampus. PMID:22723777

  5. Invariant Visual Object and Face Recognition: Neural and Computational Bases, and a Model, VisNet.

    PubMed

    Rolls, Edmund T

    2012-01-01

    Neurophysiological evidence for invariant representations of objects and faces in the primate inferior temporal visual cortex is described. Then a computational approach to how invariant representations are formed in the brain is described that builds on the neurophysiology. A feature hierarchy model in which invariant representations can be built by self-organizing learning based on the temporal and spatial statistics of the visual input produced by objects as they transform in the world is described. VisNet can use temporal continuity in an associative synaptic learning rule with a short-term memory trace, and/or it can use spatial continuity in continuous spatial transformation learning which does not require a temporal trace. The model of visual processing in the ventral cortical stream can build representations of objects that are invariant with respect to translation, view, size, and also lighting. The model has been extended to provide an account of invariant representations in the dorsal visual system of the global motion produced by objects such as looming, rotation, and object-based movement. The model has been extended to incorporate top-down feedback connections to model the control of attention by biased competition in, for example, spatial and object search tasks. The approach has also been extended to account for how the visual system can select single objects in complex visual scenes, and how multiple objects can be represented in a scene. The approach has also been extended to provide, with an additional layer, for the development of representations of spatial scenes of the type found in the hippocampus. PMID:22723777

  6. Natural language understanding and speech recognition for industrial vision systems

    NASA Astrophysics Data System (ADS)

    Batchelor, Bruce G.

    1992-11-01

    The accepted method of programming machine vision systems for a new application is to incorporate sub-routines from a standard library into code, written specially for the given task. Typical programming languages that might be used here are Pascal, C, and assembly code, although other `conventional' (i.e., imperative) languages are often used instead. The representation of an algorithm to recognize a certain object, in the form of, say, a C language program is clumsy and unnatural, compared to the alternative process of describing the object itself and leaving the software to search for it. The latter method, known as declarative programming, is used extensively both when programming in Prolog and when people talk to one another in English, or other natural languages. Programs to understand a limited sub-set of a natural language can also be written conveniently in Prolog. The article considers the prospects for talking to an image processing system, using only slightly constrained English. Moderately priced speech recognition devices, which interface to a standard desk-top computer and provide a limited repertoire (200 words) as well as the ability to identify isolated words, are already available commercially. At the moment, the goal of talking in English to a computer is incompletely fulfilled. Yet, sufficient progress has been made to encourage greater effort in this direction.

  7. Cortical plasticity for visuospatial processing and object recognition in deaf and hearing signers.

    PubMed

    Weisberg, Jill; Koo, Daniel S; Crain, Kelly L; Eden, Guinevere F

    2012-03-01

    Experience-dependent plasticity in deaf participants has been shown in a variety of studies focused on either the dorsal or ventral aspects of the visual system, but both systems have never been investigated in concert. Using functional magnetic resonance imaging (fMRI), we investigated functional plasticity for spatial processing (a dorsal visual pathway function) and for object processing (a ventral visual pathway function) concurrently, in the context of differing sensory (auditory deprivation) and language (use of a signed language) experience. During scanning, deaf native users of American Sign Language (ASL), hearing native ASL users, and hearing participants without ASL experience attended to either the spatial arrangement of frames containing objects or the identity of the objects themselves. These two tasks revealed the expected dorsal/ventral dichotomy for spatial versus object processing in all groups. In addition, the object identity matching task contained both face and house stimuli, allowing us to examine category-selectivity in the ventral pathway in all three participant groups. When contrasting the groups we found that deaf signers differed from the two hearing groups in dorsal pathway parietal regions involved in spatial cognition, suggesting sensory experience-driven plasticity. Group differences in the object processing system indicated that responses in the face-selective right lateral fusiform gyrus and anterior superior temporal cortex were sensitive to a combination of altered sensory and language experience, whereas responses in the amygdala were more closely tied to sensory experience. By selectively engaging the dorsal and ventral visual pathways within participants in groups with different sensory and language experiences, we have demonstrated that these experiences affect the function of both of these systems, and that certain changes are more closely tied to sensory experience, while others are driven by the combination of sensory and

  8. A Single-System Model Predicts Recognition Memory and Repetition Priming in Amnesia

    PubMed Central

    Kessels, Roy P.C.; Wester, Arie J.; Shanks, David R.

    2014-01-01

    We challenge the claim that there are distinct neural systems for explicit and implicit memory by demonstrating that a formal single-system model predicts the pattern of recognition memory (explicit) and repetition priming (implicit) in amnesia. In the current investigation, human participants with amnesia categorized pictures of objects at study and then, at test, identified fragmented versions of studied (old) and nonstudied (new) objects (providing a measure of priming), and made a recognition memory judgment (old vs new) for each object. Numerous results in the amnesic patients were predicted in advance by the single-system model, as follows: (1) deficits in recognition memory and priming were evident relative to a control group; (2) items judged as old were identified at greater levels of fragmentation than items judged new, regardless of whether the items were actually old or new; and (3) the magnitude of the priming effect (the identification advantage for old vs new items) overall was greater than that of items judged new. Model evidence measures also favored the single-system model over two formal multiple-systems models. The findings support the single-system model, which explains the pattern of recognition and priming in amnesia primarily as a reduction in the strength of a single dimension of memory strength, rather than a selective explicit memory system deficit. PMID:25122896

  9. A single-system model predicts recognition memory and repetition priming in amnesia.

    PubMed

    Berry, Christopher J; Kessels, Roy P C; Wester, Arie J; Shanks, David R

    2014-08-13

    We challenge the claim that there are distinct neural systems for explicit and implicit memory by demonstrating that a formal single-system model predicts the pattern of recognition memory (explicit) and repetition priming (implicit) in amnesia. In the current investigation, human participants with amnesia categorized pictures of objects at study and then, at test, identified fragmented versions of studied (old) and nonstudied (new) objects (providing a measure of priming), and made a recognition memory judgment (old vs new) for each object. Numerous results in the amnesic patients were predicted in advance by the single-system model, as follows: (1) deficits in recognition memory and priming were evident relative to a control group; (2) items judged as old were identified at greater levels of fragmentation than items judged new, regardless of whether the items were actually old or new; and (3) the magnitude of the priming effect (the identification advantage for old vs new items) overall was greater than that of items judged new. Model evidence measures also favored the single-system model over two formal multiple-systems models. The findings support the single-system model, which explains the pattern of recognition and priming in amnesia primarily as a reduction in the strength of a single dimension of memory strength, rather than a selective explicit memory system deficit.

  10. The Effect of Inversion on 3- to 5-Year-Olds' Recognition of Face and Nonface Visual Objects

    ERIC Educational Resources Information Center

    Picozzi, Marta; Cassia, Viola Macchi; Turati, Chiara; Vescovo, Elena

    2009-01-01

    This study compared the effect of stimulus inversion on 3- to 5-year-olds' recognition of faces and two nonface object categories matched with faces for a number of attributes: shoes (Experiment 1) and frontal images of cars (Experiments 2 and 3). The inversion effect was present for faces but not shoes at 3 years of age (Experiment 1). Analogous…

  11. A Temporally Distinct Role for Group I and Group II Metabotropic Glutamate Receptors in Object Recognition Memory

    ERIC Educational Resources Information Center

    Brown, Malcolm Watson; Warburton, Elizabeth Clea; Barker, Gareth Robert Isaac; Bashir, Zafar Iqbal

    2006-01-01

    Recognition memory, involving the ability to discriminate between a novel and familiar object, depends on the integrity of the perirhinal cortex (PRH). Glutamate, the main excitatory neurotransmitter in the cortex, is essential for many types of memory processes. Of the subtypes of glutamate receptor, metabotropic receptors (mGluRs) have received…

  12. Estradiol-Induced Object Recognition Memory Consolidation Is Dependent on Activation of mTOR Signaling in the Dorsal Hippocampus

    ERIC Educational Resources Information Center

    Fortress, Ashley M.; Fan, Lu; Orr, Patrick T.; Zhao, Zaorui; Frick, Karyn M.

    2013-01-01

    The mammalian target of rapamycin (mTOR) signaling pathway is an important regulator of protein synthesis and is essential for various forms of hippocampal memory. Here, we asked whether the enhancement of object recognition memory consolidation produced by dorsal hippocampal infusion of 17[Beta]-estradiol (E[subscript 2]) is dependent on mTOR…

  13. Evidence for the Activation of Sensorimotor Information during Visual Word Recognition: The Body-Object Interaction Effect

    ERIC Educational Resources Information Center

    Siakaluk, Paul D.; Pexman, Penny M.; Aguilera, Laura; Owen, William J.; Sears, Christopher R.

    2008-01-01

    We examined the effects of sensorimotor experience in two visual word recognition tasks. Body-object interaction (BOI) ratings were collected for a large set of words. These ratings assess perceptions of the ease with which a human body can physically interact with a word's referent. A set of high BOI words (e.g., "mask") and a set of low BOI…

  14. Parallel object-oriented data mining system

    DOEpatents

    Kamath, Chandrika; Cantu-Paz, Erick

    2004-01-06

    A data mining system uncovers patterns, associations, anomalies and other statistically significant structures in data. Data files are read and displayed. Objects in the data files are identified. Relevant features for the objects are extracted. Patterns among the objects are recognized based upon the features. Data from the Faint Images of the Radio Sky at Twenty Centimeters (FIRST) sky survey was used to search for bent doubles. This test was conducted on data from the Very Large Array in New Mexico which seeks to locate a special type of quasar (radio-emitting stellar object) called bent doubles. The FIRST survey has generated more than 32,000 images of the sky to date. Each image is 7.1 megabytes, yielding more than 100 gigabytes of image data in the entire data set.

  15. FACELOCK-Lock Control Security System Using Face Recognition-

    NASA Astrophysics Data System (ADS)

    Hirayama, Takatsugu; Iwai, Yoshio; Yachida, Masahiko

    A security system using biometric person authentication technologies is suited to various high-security situations. The technology based on face recognition has advantages such as lower user’s resistance and lower stress. However, facial appearances change according to facial pose, expression, lighting, and age. We have developed the FACELOCK security system based on our face recognition methods. Our methods are robust for various facial appearances except facial pose. Our system consists of clients and a server. The client communicates with the server through our protocol over a LAN. Users of our system do not need to be careful about their facial appearance.

  16. Clonal Selection Based Artificial Immune System for Generalized Pattern Recognition

    NASA Technical Reports Server (NTRS)

    Huntsberger, Terry

    2011-01-01

    The last two decades has seen a rapid increase in the application of AIS (Artificial Immune Systems) modeled after the human immune system to a wide range of areas including network intrusion detection, job shop scheduling, classification, pattern recognition, and robot control. JPL (Jet Propulsion Laboratory) has developed an integrated pattern recognition/classification system called AISLE (Artificial Immune System for Learning and Exploration) based on biologically inspired models of B-cell dynamics in the immune system. When used for unsupervised or supervised classification, the method scales linearly with the number of dimensions, has performance that is relatively independent of the total size of the dataset, and has been shown to perform as well as traditional clustering methods. When used for pattern recognition, the method efficiently isolates the appropriate matches in the data set. The paper presents the underlying structure of AISLE and the results from a number of experimental studies.

  17. An objective fingerprint quality-grading system.

    PubMed

    Pulsifer, Drew P; Muhlberger, Sarah A; Williams, Stephanie F; Shaler, Robert C; Lakhtakia, Akhlesh

    2013-09-10

    The grading of fingerprint quality by fingerprint examiners as currently practised is a subjective process. Therefore, an objective system was devised to remove the subjectivity. The devised grading system is quantitative and uses three separate, easily available, software packages to ultimately identify the portions of a fingerprint that correspond to low-, medium-, and high-quality definitive minutiae as defined on the Universal Latent Workstation of the US Federal Bureau of Investigation.

  18. Specification of Computer Systems by Objectives.

    ERIC Educational Resources Information Center

    Eltoft, Douglas

    1989-01-01

    Discusses the evolution of mainframe and personal computers, and presents a case study of a network developed at the University of Iowa called the Iowa Computer-Aided Engineering Network (ICAEN) that combines Macintosh personal computers with Apollo workstations. Functional objectives are stressed as the best measure of system performance. (LRW)

  19. Adolescent Intermittent Alcohol Exposure: Deficits in Object Recognition Memory and Forebrain Cholinergic Markers.

    PubMed

    Swartzwelder, H Scott; Acheson, Shawn K; Miller, Kelsey M; Sexton, Hannah G; Liu, Wen; Crews, Fulton T; Risher, Mary-Louise

    2015-01-01

    The long-term effects of intermittent ethanol exposure during adolescence (AIE) are of intensive interest and investigation. The effects of AIE on learning and memory and the neural functions that drive them are of particular interest as clinical findings suggest enduring deficits in those cognitive domains in humans after ethanol abuse during adolescence. Although studies of such deficits after AIE hold much promise for identifying mechanisms and therapeutic interventions, the findings are sparse and inconclusive. The present results identify a specific deficit in memory function after AIE and establish a possible neural mechanism of that deficit that may be of translational significance. Male rats (starting at PND-30) received exposure to AIE (5g/kg, i.g.) or vehicle and were allowed to mature into adulthood. At PND-71, one group of animals was assessed using the spatial-temporal object recognition (stOR) test to evaluate memory function. A separate group of animals was used to assess the density of cholinergic neurons in forebrain areas Ch1-4 using immunohistochemistry. AIE exposed animals manifested deficits in the temporal component of the stOR task relative to controls, and a significant decrease in the number of ChAT labeled neurons in forebrain areas Ch1-4. These findings add to the growing literature indicating long-lasting neural and behavioral effects of AIE that persist into adulthood and indicate that memory-related deficits after AIE depend upon the tasks employed, and possibly their degree of complexity. Finally, the parallel finding of diminished cholinergic neuron density suggests a possible mechanism underlying the effects of AIE on memory and hippocampal function as well as possible therapeutic or preventive strategies for AIE. PMID:26529506

  20. Adolescent Intermittent Alcohol Exposure: Deficits in Object Recognition Memory and Forebrain Cholinergic Markers

    PubMed Central

    Swartzwelder, H. Scott; Acheson, Shawn K.; Miller, Kelsey M.; Sexton, Hannah G.; Liu, Wen; Crews, Fulton T.; Risher, Mary-Louise

    2015-01-01

    The long-term effects of intermittent ethanol exposure during adolescence (AIE) are of intensive interest and investigation. The effects of AIE on learning and memory and the neural functions that drive them are of particular interest as clinical findings suggest enduring deficits in those cognitive domains in humans after ethanol abuse during adolescence. Although studies of such deficits after AIE hold much promise for identifying mechanisms and therapeutic interventions, the findings are sparse and inconclusive. The present results identify a specific deficit in memory function after AIE and establish a possible neural mechanism of that deficit that may be of translational significance. Male rats (starting at PND-30) received exposure to AIE (5g/kg, i.g.) or vehicle and were allowed to mature into adulthood. At PND-71, one group of animals was assessed using the spatial-temporal object recognition (stOR) test to evaluate memory function. A separate group of animals was used to assess the density of cholinergic neurons in forebrain areas Ch1-4 using immunohistochemistry. AIE exposed animals manifested deficits in the temporal component of the stOR task relative to controls, and a significant decrease in the number of ChAT labeled neurons in forebrain areas Ch1-4. These findings add to the growing literature indicating long-lasting neural and behavioral effects of AIE that persist into adulthood and indicate that memory-related deficits after AIE depend upon the tasks employed, and possibly their degree of complexity. Finally, the parallel finding of diminished cholinergic neuron density suggests a possible mechanism underlying the effects of AIE on memory and hippocampal function as well as possible therapeutic or preventive strategies for AIE. PMID:26529506

  1. Hippocampal Activation of Rac1 Regulates the Forgetting of Object Recognition Memory.

    PubMed

    Liu, Yunlong; Du, Shuwen; Lv, Li; Lei, Bo; Shi, Wei; Tang, Yikai; Wang, Lianzhang; Zhong, Yi

    2016-09-12

    Forgetting is a universal feature for most types of memories. The best-defined and extensively characterized behaviors that depict forgetting are natural memory decay and interference-based forgetting [1, 2]. Molecular mechanisms underlying the active forgetting remain to be determined for memories in vertebrates. Recent progress has begun to unravel such mechanisms underlying the active forgetting [3-11] that is induced through the behavior-dependent activation of intracellular signaling pathways. In Drosophila, training-induced activation of the small G protein Rac1 mediates natural memory decay and interference-based forgetting of aversive conditioning memory [3]. In mice, the activation of photoactivable-Rac1 in recently potentiated spines in a motor learning task erases the motor memory [12]. These lines of evidence prompted us to investigate a role for Rac1 in time-based natural memory decay and interference-based forgetting in mice. The inhibition of Rac1 activity in hippocampal neurons through targeted expression of a dominant-negative Rac1 form extended object recognition memory from less than 72 hr to over 72 hr, whereas Rac1 activation accelerated memory decay within 24 hr. Interference-induced forgetting of this memory was correlated with Rac1 activation and was completely blocked by inhibition of Rac1 activity. Electrophysiological recordings of long-term potentiation provided independent evidence that further supported a role for Rac1 activation in forgetting. Thus, Rac1-dependent forgetting is evolutionarily conserved from invertebrates to vertebrates.

  2. Hippocampal Activation of Rac1 Regulates the Forgetting of Object Recognition Memory.

    PubMed

    Liu, Yunlong; Du, Shuwen; Lv, Li; Lei, Bo; Shi, Wei; Tang, Yikai; Wang, Lianzhang; Zhong, Yi

    2016-09-12

    Forgetting is a universal feature for most types of memories. The best-defined and extensively characterized behaviors that depict forgetting are natural memory decay and interference-based forgetting [1, 2]. Molecular mechanisms underlying the active forgetting remain to be determined for memories in vertebrates. Recent progress has begun to unravel such mechanisms underlying the active forgetting [3-11] that is induced through the behavior-dependent activation of intracellular signaling pathways. In Drosophila, training-induced activation of the small G protein Rac1 mediates natural memory decay and interference-based forgetting of aversive conditioning memory [3]. In mice, the activation of photoactivable-Rac1 in recently potentiated spines in a motor learning task erases the motor memory [12]. These lines of evidence prompted us to investigate a role for Rac1 in time-based natural memory decay and interference-based forgetting in mice. The inhibition of Rac1 activity in hippocampal neurons through targeted expression of a dominant-negative Rac1 form extended object recognition memory from less than 72 hr to over 72 hr, whereas Rac1 activation accelerated memory decay within 24 hr. Interference-induced forgetting of this memory was correlated with Rac1 activation and was completely blocked by inhibition of Rac1 activity. Electrophysiological recordings of long-term potentiation provided independent evidence that further supported a role for Rac1 activation in forgetting. Thus, Rac1-dependent forgetting is evolutionarily conserved from invertebrates to vertebrates. PMID:27593377

  3. A novel delayed non-match to sample object recognition task that allows simultaneous in vivo microdialysis.

    PubMed

    Ihalainen, Jouni; Sarajärvi, Timo; Kemppainen, Susanna; Keski-Rahkonen, Pekka; Lehtonen, Marko; Tanila, Heikki

    2010-06-15

    We present a modification of the widely used delayed non-match to sample (DNMS) paradigm for assessment of object recognition memory that can be combined with simultaneous in vivo microdialysis. The present study provides evidence that hippocampal ACh release increases from baseline during active exploration of the test environment and an empty test board, but a specific further increase is seen during the recognition memory task performance. This novel experimental model offers a good tool to study the impact of selective lesions or pharmacological manipulation simultaneously on neurotransmitter levels and memory task performance.

  4. Color Image Processing and Object Tracking System

    NASA Technical Reports Server (NTRS)

    Klimek, Robert B.; Wright, Ted W.; Sielken, Robert S.

    1996-01-01

    This report describes a personal computer based system for automatic and semiautomatic tracking of objects on film or video tape, developed to meet the needs of the Microgravity Combustion and Fluids Science Research Programs at the NASA Lewis Research Center. The system consists of individual hardware components working under computer control to achieve a high degree of automation. The most important hardware components include 16-mm and 35-mm film transports, a high resolution digital camera mounted on a x-y-z micro-positioning stage, an S-VHS tapedeck, an Hi8 tapedeck, video laserdisk, and a framegrabber. All of the image input devices are remotely controlled by a computer. Software was developed to integrate the overall operation of the system including device frame incrementation, grabbing of image frames, image processing of the object's neighborhood, locating the position of the object being tracked, and storing the coordinates in a file. This process is performed repeatedly until the last frame is reached. Several different tracking methods are supported. To illustrate the process, two representative applications of the system are described. These applications represent typical uses of the system and include tracking the propagation of a flame front and tracking the movement of a liquid-gas interface with extremely poor visibility.

  5. High-Precise and Robust Face-Recognition System Based on Optical Parallel Correlator

    NASA Astrophysics Data System (ADS)

    Kodate, Kashiko

    2005-10-01

    Facial recognition is applied in a wide range of security systems, and has been studied since the 1970s, with extensive research into and development of digital processing. However, there is only available a 1:1 verification system combined with ID card identification, or an ID-less system with a small number of images in the database. The number of images that can be stored is limited, and recognition has to be improved to account for photos taken at different angles. Commercially available facial recognition systems for the most part utilize digital computers performing electronic pattern recognition. In contrast, optical analog operations can process two-dimensional images instantaneously in parallel using a lens-based Fourier transform function. In the 1960s two methods were proposed, the Vanderlugt correlator and the joint transform correlator (JTC). We present a new scheme using a multi-channel parallel JTC to make better use of spatial parallelism, through the use of a diffraction-type multi-level zone-plate array to extend a single-channel JTC. Our project's objectives were: (i) to design a matched filter which equips the system with high recognition capability at a faster calculation speed by analyzing the spatial frequency of facial image elements, and (ii) to create a four-channel Vanderlugt correlator with super-high-speed (1000 frame/s) optical parallel facial recognition system, robust enough for 1:N identification, for a large database with 4000 images. Automation was also achieved for the entire process via a practical controlling system. The achieved super-high-speed facial recognition system based on optical parallelism is faster in its processing time than the JTC optical correlator.

  6. Activity and function recognition for moving and static objects in urban environments from wide-area persistent surveillance inputs

    NASA Astrophysics Data System (ADS)

    Levchuk, Georgiy; Bobick, Aaron; Jones, Eric

    2010-04-01

    In this paper, we describe results from experimental analysis of a model designed to recognize activities and functions of moving and static objects from low-resolution wide-area video inputs. Our model is based on representing the activities and functions using three variables: (i) time; (ii) space; and (iii) structures. The activity and function recognition is achieved by imposing lexical, syntactic, and semantic constraints on the lower-level event sequences. In the reported research, we have evaluated the utility and sensitivity of several algorithms derived from natural language processing and pattern recognition domains. We achieved high recognition accuracy for a wide range of activity and function types in the experiments using Electro-Optical (EO) imagery collected by Wide Area Airborne Surveillance (WAAS) platform.

  7. Neural Mechanisms and Information Processing in Recognition Systems

    PubMed Central

    Ozaki, Mamiko; Hefetz, Abraham

    2014-01-01

    Nestmate recognition is a hallmark of social insects. It is based on the match/mismatch of an identity signal carried by members of the society with that of the perceiving individual. While the behavioral response, amicable or aggressive, is very clear, the neural systems underlying recognition are not fully understood. Here we contrast two alternative hypotheses for the neural mechanisms that are responsible for the perception and information processing in recognition. We focus on recognition via chemical signals, as the common modality in social insects. The first, classical, hypothesis states that upon perception of recognition cues by the sensory system the information is passed as is to the antennal lobes and to higher brain centers where the information is deciphered and compared to a neural template. Match or mismatch information is then transferred to some behavior-generating centers where the appropriate response is elicited. An alternative hypothesis, that of “pre-filter mechanism”, posits that the decision as to whether to pass on the information to the central nervous system takes place in the peripheral sensory system. We suggest that, through sensory adaptation, only alien signals are passed on to the brain, specifically to an “aggressive-behavior-switching center”, where the response is generated if the signal is above a certain threshold. PMID:26462936

  8. Low Energy Physical Activity Recognition System on Smartphones

    PubMed Central

    Morillo, Luis Miguel Soria; Gonzalez-Abril, Luis; Ramirez, Juan Antonio Ortega; de la Concepcion, Miguel Angel Alvarez

    2015-01-01

    An innovative approach to physical activity recognition based on the use of discrete variables obtained from accelerometer sensors is presented. The system first performs a discretization process for each variable, which allows efficient recognition of activities performed by users using as little energy as possible. To this end, an innovative discretization and classification technique is presented based on the χ2 distribution. Furthermore, the entire recognition process is executed on the smartphone, which determines not only the activity performed, but also the frequency at which it is carried out. These techniques and the new classification system presented reduce energy consumption caused by the activity monitoring system. The energy saved increases smartphone usage time to more than 27 h without recharging while maintaining accuracy. PMID:25742171

  9. Solar system object observations with Gaia Mission

    NASA Astrophysics Data System (ADS)

    Kudryashova, Maria; Tanga, Paolo; Mignard, Francois; CARRY, Benoit; Christophe, Ordenovic; DAVID, Pedro; Hestroffer, Daniel

    2016-05-01

    After a commissioning period, the astrometric mission Gaia of the European Space Agency (ESA) started its survey in July 2014. Throughout passed two years the Gaia Data Processing and Analysis Consortium (DPAC) has been treating the data. The current schedule anticipates the first Gaia Data Release (Gaia-DR1) toward the end of summer 2016. Nevertheless, it is not planned to include Solar System Objects (SSO) into the first release. This is due to a special treatment required by solar system objects, as well as by other peculiar sources (multiple and extended ones). In this presentation, we address issues and recent achivements in SSO processing, in particular validation of SSO-short term data processing chain, GAIA-SSO alerts, as well as the first runs of SSO-long term pipeline.

  10. Adaptive Optics Imaging of Solar System Objects

    NASA Technical Reports Server (NTRS)

    Roddier, Francois; Owen, Toby

    1997-01-01

    Most solar system objects have never been observed at wavelengths longer than the R band with an angular resolution better than 1 sec. The Hubble Space Telescope itself has only recently been equipped to observe in the infrared. However, because of its small diameter, the angular resolution is lower than that one can now achieved from the ground with adaptive optics, and time allocated to planetary science is limited. We have been using adaptive optics (AO) on a 4-m class telescope to obtain 0.1 sec resolution images solar system objects at far red and near infrared wavelengths (0.7-2.5 micron) which best discriminate their spectral signatures. Our efforts has been put into areas of research for which high angular resolution is essential, such as the mapping of Titan and of large asteroids, the dynamics and composition of Neptune stratospheric clouds, the infrared photometry of Pluto, Charon, and close satellites previously undetected from the ground.

  11. Adaptive Optics Imaging of Solar System Objects

    NASA Technical Reports Server (NTRS)

    Roddier, Francois; Owen, Toby

    1999-01-01

    Most solar system objects have never been observed at wavelengths longer than the R band with an angular resolution better than 1". The Hubble Space Telescope itself has only recently been equipped to observe in the infrared. However, because of its small diameter, the angular resolution is lower than that one can now achieved from the ground with adaptive optics, and time allocated to planetary science is limited. We have successfully used adaptive optics on a 4-m class telescope to obtain 0.1" resolution images of solar system objects in the far red and near infrared (0.7-2.5 microns), aE wavelengths which best discl"lmlnate their spectral signatures. Our efforts have been put into areas of research for which high angular resolution is essential.

  12. An object-based interviewing system

    SciTech Connect

    Tonn, B.; Goeltz, R. ); Chiang, Tai-Lun )

    1992-01-01

    Oak Ridge National Laboratory (ORNL) has developed an object-based interviewing system (OBIS). The key design feature is that each survey question is a function, which is treated as an independent object. The Survey Manager (SM) module maintains a stack of questions and calls each question function when directed. Each question, when called, calls the Interaction Facility (IF) to set up the appropriate screen. This modular approach to automated survey design offers maximum flexibility for system development and maintenance. The software is written in Common Lisp and currently runs on Symbolics and VAX computers. The Bureau of Labor Statistics is funding a project to use OBIS to automate the Current Population Survey (CPS), an expenditure survey, and questions about intrashousehold communication to collect data to study communication and proxy response error.

  13. An object-based interviewing system

    SciTech Connect

    Tonn, B.; Goeltz, R.; Chiang, Tai-Lun

    1992-05-01

    Oak Ridge National Laboratory (ORNL) has developed an object-based interviewing system (OBIS). The key design feature is that each survey question is a function, which is treated as an independent object. The Survey Manager (SM) module maintains a stack of questions and calls each question function when directed. Each question, when called, calls the Interaction Facility (IF) to set up the appropriate screen. This modular approach to automated survey design offers maximum flexibility for system development and maintenance. The software is written in Common Lisp and currently runs on Symbolics and VAX computers. The Bureau of Labor Statistics is funding a project to use OBIS to automate the Current Population Survey (CPS), an expenditure survey, and questions about intrashousehold communication to collect data to study communication and proxy response error.

  14. The effect of scene context on episodic object recognition: parahippocampal cortex mediates memory encoding and retrieval success.

    PubMed

    Hayes, Scott M; Nadel, Lynn; Ryan, Lee

    2007-01-01

    Previous research has investigated intentional retrieval of contextual information and contextual influences on object identification and word recognition, yet few studies have investigated context effects in episodic memory for objects. To address this issue, unique objects embedded in a visually rich scene or on a white background were presented to participants. At test, objects were presented either in the original scene or on a white background. A series of behavioral studies with young adults demonstrated a context shift decrement (CSD)-decreased recognition performance when context is changed between encoding and retrieval. The CSD was not attenuated by encoding or retrieval manipulations, suggesting that binding of object and context may be automatic. A final experiment explored the neural correlates of the CSD, using functional Magnetic Resonance Imaging. Parahippocampal cortex (PHC) activation (right greater than left) during incidental encoding was associated with subsequent memory of objects in the context shift condition. Greater activity in right PHC was also observed during successful recognition of objects previously presented in a scene. Finally, a subset of regions activated during scene encoding, such as bilateral PHC, was reactivated when the object was presented on a white background at retrieval. Although participants were not required to intentionally retrieve contextual information, the results suggest that PHC may reinstate visual context to mediate successful episodic memory retrieval. The CSD is attributed to automatic and obligatory binding of object and context. The results suggest that PHC is important not only for processing of scene information, but also plays a role in successful episodic memory encoding and retrieval. These findings are consistent with the view that spatial information is stored in the hippocampal complex, one of the central tenets of Multiple Trace Theory.

  15. Infrared system for monitoring movement of objects

    DOEpatents

    Valentine, K.H.; Falter, D.D.; Falter, K.G.

    1991-04-30

    A system is described for monitoring moving objects, such as the flight of honeybees and other insects, using a pulsed laser light source. This system has a self-powered micro-miniaturized transmitting unit powered, in the preferred embodiment, with an array of solar cells. This transmitting unit is attached to the object to be monitored. These solar cells provide current to a storage energy capacitor to produce, for example, five volts for the operation of the transmitter. In the simplest embodiment, the voltage on the capacitor operates a pulse generator to provide a pulsed energizing signal to one or more very small laser diodes. The pulsed light is then received at a receiving base station using substantially standard means which converts the light to an electrical signal for processing in a microprocessor to create the information as to the movement of the object. In the case of a unit for monitoring honeybees and other insects, the transmitting unit weighs less than 50 mg, and has a size no larger than 1[times]3[times]5 millimeters. Also, the preferred embodiment provides for the coding of the light to uniquely identify the particular transmitting unit that is being monitored. A wake-up' circuit is provided in the preferred embodiment whereby there is no transmission until the voltage on the capacitor has exceeded a pre-set threshold. Various other uses of the motion-detection system are described. 4 figures.

  16. Infrared system for monitoring movement of objects

    DOEpatents

    Valentine, Kenneth H.; Falter, Diedre D.; Falter, Kelly G.

    1991-01-01

    A system for monitoring moving objects, such as the flight of honeybees and other insects, using a pulsed laser light source. This system has a self-powered micro-miniaturized transmitting unit powered, in the preferred embodiment, with an array solar cells. This transmitting unit is attached to the object to be monitored. These solar cells provide current to a storage energy capacitor to produce, for example, five volts for the operation of the transmitter. In the simplest embodiment, the voltage on the capacitor operates a pulse generator to provide a pulsed energizing signal to one or more very small laser diodes. The pulsed light is then received at a receiving base station using substantially standard means which converts the light to an electrical signal for processing in a microprocessor to create the information as to the movement of the object. In the case of a unit for monitoring honeybees and other insects, the transmitting unit weighs less than 50 mg, and has a size no larger than 1.times.3.times.5 millimeters. Also, the preferred embodiment provides for the coding of the light to uniquely identify the particular transmitting unit that is being monitored. A "wake-up" circuit is provided in the preferred embodiment whereby there is no transmission until the voltage on the capacitor has exceeded a pre-set threshold. Various other uses of the motion-detection system are described.

  17. Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes

    PubMed Central

    Yebes, J. Javier; Bergasa, Luis M.; García-Garrido, Miguel Ángel

    2015-01-01

    Driver assistance systems and autonomous robotics rely on the deployment of several sensors for environment perception. Compared to LiDAR systems, the inexpensive vision sensors can capture the 3D scene as perceived by a driver in terms of appearance and depth cues. Indeed, providing 3D image understanding capabilities to vehicles is an essential target in order to infer scene semantics in urban environments. One of the challenges that arises from the navigation task in naturalistic urban scenarios is the detection of road participants (e.g., cyclists, pedestrians and vehicles). In this regard, this paper tackles the detection and orientation estimation of cars, pedestrians and cyclists, employing the challenging and naturalistic KITTI images. This work proposes 3D-aware features computed from stereo color images in order to capture the appearance and depth peculiarities of the objects in road scenes. The successful part-based object detector, known as DPM, is extended to learn richer models from the 2.5D data (color and disparity), while also carrying out a detailed analysis of the training pipeline. A large set of experiments evaluate the proposals, and the best performing approach is ranked on the KITTI website. Indeed, this is the first work that reports results with stereo data for the KITTI object challenge, achieving increased detection ratios for the classes car and cyclist compared to a baseline DPM. PMID:25903553

  18. Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes.

    PubMed

    Yebes, J Javier; Bergasa, Luis M; García-Garrido, Miguel Ángel

    2015-01-01

    Driver assistance systems and autonomous robotics rely on the deployment of several sensors for environment perception. Compared to LiDAR systems, the inexpensive vision sensors can capture the 3D scene as perceived by a driver in terms of appearance and depth cues. Indeed, providing 3D image understanding capabilities to vehicles is an essential target in order to infer scene semantics in urban environments. One of the challenges that arises from the navigation task in naturalistic urban scenarios is the detection of road participants (e.g., cyclists, pedestrians and vehicles). In this regard, this paper tackles the detection and orientation estimation of cars, pedestrians and cyclists, employing the challenging and naturalistic KITTI images. This work proposes 3D-aware features computed from stereo color images in order to capture the appearance and depth peculiarities of the objects in road scenes. The successful part-based object detector, known as DPM, is extended to learn richer models from the 2.5D data (color and disparity), while also carrying out a detailed analysis of the training pipeline. A large set of experiments evaluate the proposals, and the best performing approach is ranked on the KITTI website. Indeed, this is the first work that reports results with stereo data for the KITTI object challenge, achieving increased detection ratios for the classes car and cyclist compared to a baseline DPM. PMID:25903553

  19. Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes.

    PubMed

    Yebes, J Javier; Bergasa, Luis M; García-Garrido, Miguel Ángel

    2015-04-20

    Driver assistance systems and autonomous robotics rely on the deployment of several sensors for environment perception. Compared to LiDAR systems, the inexpensive vision sensors can capture the 3D scene as perceived by a driver in terms of appearance and depth cues. Indeed, providing 3D image understanding capabilities to vehicles is an essential target in order to infer scene semantics in urban environments. One of the challenges that arises from the navigation task in naturalistic urban scenarios is the detection of road participants (e.g., cyclists, pedestrians and vehicles). In this regard, this paper tackles the detection and orientation estimation of cars, pedestrians and cyclists, employing the challenging and naturalistic KITTI images. This work proposes 3D-aware features computed from stereo color images in order to capture the appearance and depth peculiarities of the objects in road scenes. The successful part-based object detector, known as DPM, is extended to learn richer models from the 2.5D data (color and disparity), while also carrying out a detailed analysis of the training pipeline. A large set of experiments evaluate the proposals, and the best performing approach is ranked on the KITTI website. Indeed, this is the first work that reports results with stereo data for the KITTI object challenge, achieving increased detection ratios for the classes car and cyclist compared to a baseline DPM.

  20. A comparison of the effects of depth rotation on visual and haptic three-dimensional object recognition.

    PubMed

    Lawson, Rebecca

    2009-08-01

    A sequential matching task was used to compare how the difficulty of shape discrimination influences the achievement of object constancy for depth rotations across haptic and visual object recognition. Stimuli were nameable, 3-dimensional plastic models of familiar objects (e.g., bed, chair) and morphs midway between these endpoint shapes (e.g., a bed-chair morph). The 2 objects presented on a trial were either both placed at the same orientation or were rotated by 90 degrees relative to each other. Discrimination difficulty was increased by presenting more similarly shaped objects on mismatch trials (easy: bed, then lizard; medium: bed, then chair; hard: bed, then bed-chair morph). For within-modal visual matching, orientation changes were most disruptive when shape discrimination was hardest. This interaction for 3-dimensional objects replicated the interaction reported in earlier studies presenting 2-dimensional pictures of the same objects (Lawson & Bülthoff, 2008). In contrast, orientation changes and discrimination difficulty had additive effects on within-modal haptic and cross-modal visual-to-haptic matching, whereas cross-modal haptic-to-visual matching was orientation invariant. These results suggest that the cause of orientation sensitivity may differ for visual and haptic object recognition.

  1. Implementation study of wearable sensors for activity recognition systems

    PubMed Central

    Ghassemian, Mona

    2015-01-01

    This Letter investigates and reports on a number of activity recognition methods for a wearable sensor system. The authors apply three methods for data transmission, namely ‘stream-based’, ‘feature-based’ and ‘threshold-based’ scenarios to study the accuracy against energy efficiency of transmission and processing power that affects the mote's battery lifetime. They also report on the impact of variation of sampling frequency and data transmission rate on energy consumption of motes for each method. This study leads us to propose a cross-layer optimisation of an activity recognition system for provisioning acceptable levels of accuracy and energy efficiency. PMID:26609413

  2. Implementation study of wearable sensors for activity recognition systems.

    PubMed

    Rezaie, Hamed; Ghassemian, Mona

    2015-08-01

    This Letter investigates and reports on a number of activity recognition methods for a wearable sensor system. The authors apply three methods for data transmission, namely 'stream-based', 'feature-based' and 'threshold-based' scenarios to study the accuracy against energy efficiency of transmission and processing power that affects the mote's battery lifetime. They also report on the impact of variation of sampling frequency and data transmission rate on energy consumption of motes for each method. This study leads us to propose a cross-layer optimisation of an activity recognition system for provisioning acceptable levels of accuracy and energy efficiency.

  3. Automatic Recognition Of Moving Objects And Its Application To A Robot For Picking Asparagus

    NASA Astrophysics Data System (ADS)

    Baylou, P.; Amor, B. El Hadj; Bousseau, G.

    1983-10-01

    After a brief description of the robot for picking white asparagus, a statistical study of the different shapes of asparagus tips allowed us to determine certain discriminating parameters to detect the tips as they appear on the silhouette of the mound of earth. The localisation was done stereometrically with the help of two cameras. As the robot carrying the system of vision-localisation moves, the images are altered and decision cri-teria modified. A study of the image from mobile objects produced by both tube and CCD came-ras was carried out. A simulation of this phenomenon has been achieved in order to determine the modifications concerning object shapes, thresholding levels and decision parameters in function of the robot speed.

  4. Design of speaker recognition system based on artificial neural network

    NASA Astrophysics Data System (ADS)

    Chen, Yanhong; Wang, Li; Lin, Han; Li, Jinlong

    2012-10-01

    Speaker recognition is to recognize speaker's identity from its voice which contains physiological and behavioral characteristics unique to each individual. In this paper, the artificial neural network model, which has very good capacity of non-linear division in characteristic space, is used for pattern matching. The speaker's sample characteristic domain is built for his mixed voice characteristic signals based on Kmeanlbg algorithm. Then the dimension of the inputting eigenvector is reduced, and the redundant information is got rid of. On this basis, BP neural network is used to divide capacity area for characteristic space nonlinearly, and the BP neural network acts as a classifier for the speaker. Finally, a speaker recognition system based on the neural network is realized and the experiment results validate the recognition performance and robustness of the system.

  5. NMDA Receptor Antagonist Ketamine Distorts Object Recognition by Reducing Feedback to Early Visual Cortex.

    PubMed

    van Loon, Anouk M; Fahrenfort, Johannes J; van der Velde, Bauke; Lirk, Philipp B; Vulink, Nienke C C; Hollmann, Markus W; Scholte, H Steven; Lamme, Victor A F

    2016-05-01

    It is a well-established fact that top-down processes influence neural representations in lower-level visual areas. Electrophysiological recordings in monkeys as well as theoretical models suggest that these top-down processes depend on NMDA receptor functioning. However, this underlying neural mechanism has not been tested in humans. We used fMRI multivoxel pattern analysis to compare the neural representations of ambiguous Mooney images before and after they were recognized with their unambiguous grayscale version. Additionally, we administered ketamine, an NMDA receptor antagonist, to interfere with this process. Our results demonstrate that after recognition, the pattern of brain activation elicited by a Mooney image is more similar to that of its easily recognizable grayscale version than to the pattern evoked by the identical Mooney image before recognition. Moreover, recognition of Mooney images decreased mean response; however, neural representations of separate images became more dissimilar. So from the neural perspective, unrecognizable Mooney images all "look the same", whereas recognized Mooneys look different. We observed these effects in posterior fusiform part of lateral occipital cortex and in early visual cortex. Ketamine distorted these effects of recognition, but in early visual cortex only. This suggests that top-down processes from higher- to lower-level visual areas might operate via an NMDA pathway. PMID:25662715

  6. Distributed Object Oriented Geographic Information System

    1997-02-01

    This interactive, object-oriented, distributed Geographic Information System (GIS) uses the World Wibe Web (WWW) as application medium and distribution mechanism. The software provides distributed access to multiple geo-spatial databases and presents them as if they came from a single coherent database. DOOGIS distributed access comes not only in the form of multiple geo-spatial servers but can break down a single logical server into the constituent physical servers actually storing the data. The program provides formore » dynamic protocol resolution and content handling allowing unknown objects from a particular server to download their handling code. Security and access privileges are negotiated dynamically with each server contacted and each access attempt.« less

  7. Environmental management system objectives & targets results summary :

    SciTech Connect

    Vetter, Douglas Walter

    2014-04-01

    Sandia National Laboratories/New Mexicos (SNL/NM) Environmental Management System is the integrated approach for members of the workforce to identify and manage environmental risks. Each Fiscal Year (FY) SNL/NM performs an analysis to identify environmental aspects, and the environmental programs associated with them are charged with the task of routinely monitoring and measuring the objectives and targets that are established to mitigate potential impacts of SNL/NMs operations on the environment. An annual summary of the results achieved towards meeting established Sandia Corporation and SNL/NM Site-specific objectives and targets provides a connection to, and rational for, annually revised environmental aspects. The purpose of this document is to summarize the results achieved and documented in FY2013.

  8. Real-time object recognition in multidimensional images based on joined extended structural tensor and higher-order tensor decomposition methods

    NASA Astrophysics Data System (ADS)

    Cyganek, Boguslaw; Smolka, Bogdan

    2015-02-01

    In this paper a system for real-time recognition of objects in multidimensional video signals is proposed. Object recognition is done by pattern projection into the tensor subspaces obtained from the factorization of the signal tensors representing the input signal. However, instead of taking only the intensity signal the novelty of this paper is first to build the Extended Structural Tensor representation from the intensity signal that conveys information on signal intensities, as well as on higher-order statistics of the input signals. This way the higher-order input pattern tensors are built from the training samples. Then, the tensor subspaces are built based on the Higher-Order Singular Value Decomposition of the prototype pattern tensors. Finally, recognition relies on measurements of the distance of a test pattern projected into the tensor subspaces obtained from the training tensors. Due to high-dimensionality of the input data, tensor based methods require high memory and computational resources. However, recent achievements in the technology of the multi-core microprocessors and graphic cards allows real-time operation of the multidimensional methods as is shown and analyzed in this paper based on real examples of object detection in digital images.

  9. Intelligent Facial Recognition Systems: Technology advancements for security applications

    SciTech Connect

    Beer, C.L.

    1993-07-01

    Insider problems such as theft and sabotage can occur within the security and surveillance realm of operations when unauthorized people obtain access to sensitive areas. A possible solution to these problems is a means to identify individuals (not just credentials or badges) in a given sensitive area and provide full time personnel accountability. One approach desirable at Department of Energy facilities for access control and/or personnel identification is an Intelligent Facial Recognition System (IFRS) that is non-invasive to personnel. Automatic facial recognition does not require the active participation of the enrolled subjects, unlike most other biological measurement (biometric) systems (e.g., fingerprint, hand geometry, or eye retinal scan systems). It is this feature that makes an IFRS attractive for applications other than access control such as emergency evacuation verification, screening, and personnel tracking. This paper discusses current technology that shows promising results for DOE and other security applications. A survey of research and development in facial recognition identified several companies and universities that were interested and/or involved in the area. A few advanced prototype systems were also identified. Sandia National Laboratories is currently evaluating facial recognition systems that are in the advanced prototype stage. The initial application for the evaluation is access control in a controlled environment with a constant background and with cooperative subjects. Further evaluations will be conducted in a less controlled environment, which may include a cluttered background and subjects that are not looking towards the camera. The outcome of the evaluations will help identify areas of facial recognition systems that need further development and will help to determine the effectiveness of the current systems for security applications.

  10. SOLAR SYSTEM OBJECTS AS COSMIC RAYS DETECTORS

    SciTech Connect

    Privitera, P.; Motloch, P.

    2014-08-10

    In a recent Letter, Jupiter is presented as an efficient detector for Ultra-High Energy Cosmic Rays (UHECRs), through measurement by an Earth-orbiting satellite of gamma rays from UHECRs showers produced in Jupiter's atmosphere. We show that this result is incorrect, due to erroneous assumptions on the angular distribution of shower particles. We evaluated other solar system objects as potential targets for UHECRs detection, and found that the proposed technique is either not viable or not competitive with traditional ground-based UHECRs detectors.

  11. Color recognition system for urine analyzer

    NASA Astrophysics Data System (ADS)

    Zhu, Lianqing; Wang, Zicai; Lin, Qian; Dong, Mingli

    2010-08-01

    In order to increase the speed of photoelectric conversion, a linear CCD is applied as the photoelectric converter instead of the traditional photodiode. A white LED is used as the light source of the system. The color information of the urine test strip is transferred into the CCD through a reflecting optical system. It is then converted to digital signals by an A/D converter. The test results of urine analysis are obtained by a data processing system. An ARM microprocessor is selected as the CPU of the system and a CPLD is employed to provide a driving timing for the CCD drive and the A/D converter. Active HDL7.2 and Verilog HDL are used to simulate the driving timing of the CPLD. Experimental results show that the correctness rate of the test results is better than 90%. The system satisfies the requirements of the color information collection of urine analyzer.

  12. New taste sensor system combined with chaotic recognition

    NASA Astrophysics Data System (ADS)

    Hu, Jie; Wang, Ping; Li, Rong

    2001-09-01

    Taste sensor as a new kind of chemical sensor has been studied by many researchers. We have developed several types of taste sensor system and some new recognition methods for taste substance. Kiyoshi Toko et al proposed a new kind of chaos taste sensor that is based on sensor chaos dynamics. In this paper, we improve the taste sensor based on chaos dynamics and proposed a new method for the pattern recognition of tastes. We use three kinds of tastes, i.e., sweetness, salty taste, and sourness. They cause the membrane oscillate in different form, and the complexity is not the same. We can detect taste based on the new method.

  13. Design and implementation of face recognition system based on Windows

    NASA Astrophysics Data System (ADS)

    Zhang, Min; Liu, Ting; Li, Ailan

    2015-07-01

    In view of the basic Windows login password input way lacking of safety and convenient operation, we will introduce the biometrics technology, face recognition, into the computer to login system. Not only can it encrypt the computer system, also according to the level to identify administrators at all levels. With the enhancement of the system security, user input can neither be a cumbersome nor worry about being stolen password confidential.

  14. Optical inspection system for cylindrical objects

    DOEpatents

    Brenden, Byron B.; Peters, Timothy J.

    1989-01-01

    In the inspection of cylindrical objects, particularly O-rings, the object is translated through a field of view and a linear light trace is projected on its surface. An image of the light trace is projected on a mask, which has a size and shape corresponding to the size and shape which the image would have if the surface of the object were perfect. If there is a defect, light will pass the mask and be sensed by a detector positioned behind the mask. Preferably, two masks and associated detectors are used, one mask being convex to pass light when the light trace falls on a projection from the surface and the other concave, to pass light when the light trace falls on a depression in the surface. The light trace may be either dynamic, formed by a scanned laser beam, or static, formed by such a beam focussed by a cylindrical lens. Means are provided to automatically keep the illuminating receiving systems properly aligned.

  15. An Overview of Hand Gestures Recognition System Techniques

    NASA Astrophysics Data System (ADS)

    Farhana Mod Ma'asum, Farah; Sulaiman, Suhana; Saparon, Azilah

    2015-11-01

    Hand gesture recognition system has evolved tremendously in the recent few years because of its ability to interact with machine efficiently. Mankind tries to incorporate human gestures into modern technology by searching and finding a replacement of multi touch technology which does not require any touching movement on screen. This paper presents an overview on several methods to realize hand gesture recognition by using three main modules: camera and segmentation module, detection module and feature extraction module. There are many methods which can be used to get the respective results depending on its advantages. Summary of previous research and results of hand gesture methods as well as comparison between gesture recognition are also given in this paper.

  16. Astrometry of Solar System Objects with Gaia

    NASA Astrophysics Data System (ADS)

    Hestroffer, Daniel J.; Arenou, Frederic; Desmars, Josselin; Robert, Vincent; Thuillot, William; Arlot, Jean-Eudes; Carry, Benoit; David, Pedro; Eggl, Siegfried; Fabricius, Claus; Kudryashova, Maria; Lainey, Valery; Spoto, Federica; Tanga, Paolo; Gaia DPAC

    2016-10-01

    The Gaia ESA space mission will provide astrometric observations of a large number of celestial bodies, with unprecedented accuracy, and in an homogenous reference frame (to become the optical ICRF). The Gaia satellite is monitoring regularly the whole celestial sphere, with one complete scan in about 6month, down to approximately magnitude V≤20.7. It will provide after its nominal lifetime, (5 years, 2014-2019) about 70 astrometric points for several hundred thousands of solar system objects, asteroids from the Near-Earth region to Centaurs and bright TNOs, as well as planetary satellites and comets. The highly precise astrometric and photometric data is bound to lead to huge advances in the science of small Small Solar System Bodies (e.g. Tanga et al. 2016 P\\&SS, Hestroffer et al. 2014 COSPAR #40 ; Mignard et al. 2007 EMP).The first Gaia data release (GDR#1) is foreseen for Q3-2016 and will provide highly precise positions of selected stars down to mag V≈20. While solar system objets data is foreseen for the next data release (in 2017), science of Solar System will also highly benefit from the Gaia stellar catalogue. We will present the status of the satellite and Gaia mission, and details on the stellar data that will be published in this GDR#1. We discuss the catalogue content, number of stars, parameters and precisions, and the process of cross-matching and validation. We also touch upon the construction of combined Tycho-Gaia TGAS catalogue.A Gaia data daily processing is devoted to the identification of Solar System Objects. During this process the detection of new (or critical) objects arises and leads to the triggering of scientific alerts to be found on the web gaiafunsso.imcce.fr. We have also set up an international follow-up network called Gaia-FUN-SSO to validate the detection in space. For this goal, in case of detection the observational data must be sent to the MPC by the observers. Besides, Gaia should benefit for the classical astrometric

  17. Object representation in the human auditory system

    PubMed Central

    Winkler, István; van Zuijen, Titia L.; Sussman, Elyse; Horváth, János; Näätänen, Risto

    2010-01-01

    One important principle of object processing is exclusive allocation. Any part of the sensory input, including the border between two objects, can only belong to one object at a time. We tested whether tones forming a spectro-temporal border between two sound patterns can belong to both patterns at the same time. Sequences were composed of low-, intermediate- and high-pitched tones. Tones were delivered with short onset-to-onset intervals causing the high and low tones to automatically form separate low and high sound streams. The intermediate-pitch tones could be perceived as part of either one or the other stream, but not both streams at the same time. Thus these tones formed a pitch ’border’ between the two streams. The tones were presented in a fixed, cyclically repeating order. Linking the intermediate-pitch tones with the high or the low tones resulted in the perception of two different repeating tonal patterns. Participants were instructed to maintain perception of one of the two tone patterns throughout the stimulus sequences. Occasional changes violated either the selected or the alternative tone pattern, but not both at the same time. We found that only violations of the selected pattern elicited the mismatch negativity event-related potential, indicating that only this pattern was represented in the auditory system. This result suggests that individual sounds are processed as part of only one auditory pattern at a time. Thus tones forming a spectro-temporal border are exclusively assigned to one sound object at any given time, as are spatio-temporal borders in vision. PMID:16836636

  18. Body posture recognition and turning recording system for the care of bed bound patients.

    PubMed

    Hsiao, Rong-Shue; Mi, Zhenqiang; Yang, Bo-Ru; Kau, Lih-Jen; Bitew, Mekuanint Agegnehu; Li, Tzu-Yu

    2015-01-01

    This paper proposes body posture recognition and turning recording system for assisting the care of bed bound patients in nursing homes. The system continuously detects the patient's body posture and records the length of time for each body posture. If the patient remains in the same body posture long enough to develop pressure ulcers, the system notifies caregivers to change the patient's body posture. The objective of recording is to provide the log of body turning for querying of patients' family members. In order to accurately detect patient's body posture, we developed a novel pressure sensing pad which contains force sensing resistor sensors. Based on the proposed pressure sensing pad, we developed a bed posture recognition module which includes a bed posture recognition algorithm. The algorithm is based on fuzzy theory. The body posture recognition algorithm can detect the patient's bed posture whether it is right lateral decubitus, left lateral decubitus, or supine. The detected information of patient's body posture can be then transmitted to the server of healthcare center by the communication module to perform the functions of recording and notification. Experimental results showed that the average posture recognition accuracy for our proposed module is 92%. PMID:26444814

  19. ROSETTA: the compile-time recognition of object-oriented library abstractions and their use within user applications

    SciTech Connect

    Quinlan, D; Philip, B

    2001-01-08

    Libraries arise naturally from the increasing complexity of developing scientific applications, the optimization of libraries is just one type of high-performance optimization. Many complex applications areas can today be addressed by domain-specific object-oriented frameworks. Such object-oriented frameworks provide an effective compliment to an object-oriented language and effectively permit the design of what amount to essentially domain-specific languages. The optimization of such a domain-specific library/language combination however is particularly complicated due to the inability of the compiler to optimize the use of the libraries abstractions. The recognition of the use of object-oriented abstractions within user applications is a particularly difficult but important step in the optimization of how objects are used within expressions and statements. Such recognition entails more than just complex pattern matching. The approach presented within this paper uses specially built grammars to parse the C++ representation. The C++ representation is itself obtained using a modified version of the SAGE II C/C++ source code restructuring tool which is inturn based upon the Edison Design Group (EDG) C++ front-end. ROSETTA is a tool which automatically builds grammars and parsers from class definitions, associated parsers parse abstract syntax trees (ASTs) of lower level grammars into ASTs of higher level grammars. The lowest level grammar is that associated with the full C++ language itself, higher level grammars specialize the grammars specific to user defined objects. The grammars form a hierarchy and permit a high-degree of specialization in the recognition of complex use of user defined abstractions.

  20. Voice-Recognition System Records Inspection Data

    NASA Technical Reports Server (NTRS)

    Rochester, Larry L.

    1993-01-01

    Main Injector Voice Activated Record (MIVAR) system acts on vocal commands and processes spoken inspection data into electronic and printed inspection reports. Devised to improve acquisition and recording of data from borescope inspections of interiors of liquid-oxygen-injecting tubes on main engine of Space Shuttle. With modifications, system used in other situations to relieve inspectors of manual recording of data. Enhances flow of work and quality of data acquired by enabling inspector to remain visually focused on workpiece.

  1. The development of object recognition memory in rhesus macaques with neonatal lesions of the perirhinal cortex.

    PubMed

    Zeamer, Alyson; Richardson, Rebecca L; Weiss, Alison R; Bachevalier, Jocelyne

    2015-02-01

    To investigate the role of the perirhinal cortex on the development of recognition measured by the visual paired-comparison (VPC) task, infant monkeys with neonatal perirhinal lesions and sham-operated controls were tested at 1.5, 6, 18, and 48 months of age on the VPC task with color stimuli and intermixed delays of 10 s, 30 s, 60 s, and 120 s. Monkeys with neonatal perirhinal lesions showed an increase in novelty preference between 1.5 and 6 months of age similar to controls, although at these two ages, performance remained significantly poorer than that of control animals. With age, performance in animals with neonatal perirhinal lesions deteriorated as compared to that of controls. In contrast to the lack of novelty preference in monkeys with perirhinal lesions acquired in adulthood, novelty preference in the neonatally operated animals remained above chance at all delays and all ages. The data suggest that, although incidental recognition memory processes can be supported by the perirhinal cortex in early infancy, other temporal cortical areas may support these processes in the absence of a functional perirhinal cortex early in development. The neural substrates mediating incidental recognition memory processes appear to be more widespread in early infancy than in adulthood. PMID:25096364

  2. A signal and image processing object-based system using CLOS. [Common LISP Object System (CLOS)

    SciTech Connect

    Hernandez, J.E.; Lu, Shin-Yee; Sherwood, R.J.; Clark, G.A.; Lawver, B.S.

    1991-09-01

    This paper presents a LISP based system for signal and image processing. Using an object based approach the system integrates signal and image processing algorithms, supervised and unsupervised neural network algorithms, and mild-level computer vision capabilities, into a cohesive framework. This framework is suitable for prototyping complex algorithms dealing with multiple classes of data. The system, known as VISION, is currently used as a prototyping environment for wide range of scientific applications internal to LLNL. This paper highlights some of the capabilities of VISION, and how they were implemented using the Common LISP Object System, CLOS. 13 refs.

  3. Combining soft decision algorithms and scale-sequential hypotheses pruning for object recognition

    SciTech Connect

    Kumar, V.P.; Manolakos, E.S.

    1996-12-31

    This paper describes a system that exploits the synergy of Hierarchical Mixture Density (HMD) estimation with multiresolution decomposition based hypothesis pruning to perform efficiently joint segmentation and labeling of partially occluded objects in images. First we present the overall structure of the HMD estimation algorithm in the form of a recurrent neural network which generates the posterior probabilities of the various hypotheses associated with the image. Then in order to reduce the large memory and computation requirement we propose a hypothesis pruning scheme making use of the orthonormal discrete wavelet transform for dimensionality reduction. We provide an intuitive justification for the validity of this scheme and present experimental results and performance analysis on real and synthetic images to verify our claims.

  4. Recognition of bacterial plant pathogens: local, systemic and transgenerational immunity.

    PubMed

    Henry, Elizabeth; Yadeta, Koste A; Coaker, Gitta

    2013-09-01

    Bacterial pathogens can cause multiple plant diseases and plants rely on their innate immune system to recognize and actively respond to these microbes. The plant innate immune system comprises extracellular pattern recognition receptors that recognize conserved microbial patterns and intracellular nucleotide binding leucine-rich repeat (NLR) proteins that recognize specific bacterial effectors delivered into host cells. Plants lack the adaptive immune branch present in animals, but still afford flexibility to pathogen attack through systemic and transgenerational resistance. Here, we focus on current research in plant immune responses against bacterial pathogens. Recent studies shed light onto the activation and inactivation of pattern recognition receptors and systemic acquired resistance. New research has also uncovered additional layers of complexity surrounding NLR immune receptor activation, cooperation and sub-cellular localizations. Taken together, these recent advances bring us closer to understanding the web of molecular interactions responsible for coordinating defense responses and ultimately resistance.

  5. System integration of pattern recognition, adaptive aided, upper limb prostheses

    NASA Technical Reports Server (NTRS)

    Lyman, J.; Freedy, A.; Solomonow, M.

    1975-01-01

    The requirements for successful integration of a computer aided control system for multi degree of freedom artificial arms are discussed. Specifications are established for a system which shares control between a human amputee and an automatic control subsystem. The approach integrates the following subsystems: (1) myoelectric pattern recognition, (2) adaptive computer aiding; (3) local reflex control; (4) prosthetic sensory feedback; and (5) externally energized arm with the functions of prehension, wrist rotation, elbow extension and flexion and humeral rotation.

  6. Earth Observing System: Science Objectives and Challenges

    NASA Technical Reports Server (NTRS)

    King, Michael D.

    1998-01-01

    The Earth Observing System (EOS) is a space-based observing system comprised of a series of satellite sensors by which scientists can monitor the Earth, a Data and Information System (EOSDIS) enabling researchers worldwide to access the satellite data, and an interdisciplinary science research program to interpret the satellite data. In this presentation I will describe the key areas of scientific uncertainty in understanding climate and global change, and follow that with a description of the EOS goals, objectives, and scientific research elements that comprise the program (instrument science teams and interdisciplinary investigations). Finally, I will describe how scientists and policy makers intend to use EOS data to improve our understanding of key global change uncertainties, such as: (i) clouds and radiation, including fossil fuel and natural emissions of sulfate aerosol and its potential impact on cloud feedback, (ii) man's impact on ozone depletion, with examples of ClO and O3 obtained from the UARS satellite during the Austral Spring, and (iii) volcanic eruptions and their impact on climate, with examples from the eruption of Mt. Pinatubo.

  7. Earth Observing System: Science Objectives and Challenges

    NASA Technical Reports Server (NTRS)

    King, Michael D.

    1999-01-01

    The Earth Observing System (EOS) is a space-based observing system comprised of a series of satellite sensors by which scientists can monitor the Earth, a Data and Information System (EOSDIS) enabling researchers worldwide to access the satellite data, and an interdisciplinary science research program to interpret the satellite data. In this presentation we review the key areas of scientific uncertainty in understanding climate and global change, and follow that with a description of the EOS goals, objectives, and scientific research elements that comprise the program (instrument science teams and interdisciplinary investigations). Finally, I will describe how scientists and policy makers intend to use EOS data improve our understanding of key global change uncertainties, such as: (i) clouds and radiation, including fossil fuel and natural emissions of sulfate aerosol and its potential impact on cloud feedback, (ii) man's impact on ozone depletion, with examples of ClO and O3 obtained from the UARS satellite during the Austral Spring, and (iii) volcanic eruptions and their impact on climate, with examples from the eruption of Mt. Pinatubo.

  8. GTOSS: Generalized Tethered Object Simulation System

    NASA Technical Reports Server (NTRS)

    Lang, David D.

    1987-01-01

    GTOSS represents a tether analysis complex which is described by addressing its family of modules. TOSS is a portable software subsystem specifically designed to be introduced into the environment of any existing vehicle dynamics simulation to add the capability of simulating multiple interacting objects (via multiple tethers). These objects may interact with each other as well as with the vehicle into whose environment TOSS is introduced. GTOSS is a stand alone tethered system analysis program, representing an example of TOSS having been married to a host simulation. RTOSS is the Results Data Base (RDB) subsystem designed to archive TOSS simulation results for future display processing. DTOSS is a display post processors designed to utilize the RDB. DTOSS extracts data from the RDB for multi-page printed time history displays. CTOSS is similar to DTOSS, but is designed to create ASCII plot files. The same time history data formats provided for DTOSS (for printing) are available via CTOSS for plotting. How these and other modules interact with each other is discussed.

  9. A Novel Word Based Arabic Handwritten Recognition System Using SVM Classifier

    NASA Astrophysics Data System (ADS)

    Khalifa, Mahmoud; Bingru, Yang

    Every language script has its structure, characteristic, and feature. Character based word recognition depends on the feature available to be extracted from character. Word based script recognition overcome the problem of character segmenting and can be applied for several languages (Arabic, Urdu, Farsi... est.). In this paper Arabic handwritten is classified as word based system. Firstly, words segmented and normalized in size to fit the DCT input. Then extract feature characteristic by computing the Euclidean distance between pairs of objects in n-by-m data matrix X. Based on the point's operator of extrema, feature was extracted. Then apply one to one-Class Support Vector Machines (SVMs) as a discriminative framework in order to address feature classification. The approach was tested with several public databases and we get high efficiency rate recognition.

  10. What Response Properties Do Individual Neurons Need to Underlie Position and Clutter “Invariant” Object Recognition?

    PubMed Central

    Li, Nuo; Cox, David D.; Zoccolan, Davide; DiCarlo, James J.

    2009-01-01

    Primates can easily identify visual objects over large changes in retinal position—a property commonly referred to as position “invariance.” This ability is widely assumed to depend on neurons in inferior temporal cortex (IT) that can respond selectively to isolated visual objects over similarly large ranges of retinal position. However, in the real world, objects rarely appear in isolation, and the interplay between position invariance and the representation of multiple objects (i.e., clutter) remains unresolved. At the heart of this issue is the intuition that the representations of nearby objects can interfere with one another and that the large receptive fields needed for position invariance can exacerbate this problem by increasing the range over which interference acts. Indeed, most IT neurons' responses are strongly affected by the presence of clutter. While external mechanisms (such as attention) are often invoked as a way out of the problem, we show (using recorded neuronal data and simulations) that the intrinsic properties of IT population responses, by themselves, can support object recognition in the face of limited clutter. Furthermore, we carried out extensive simulations of hypothetical neuronal populations to identify the essential individual-neuron ingredients of a good population representation. These simulations show that the crucial neuronal property to support recognition in clutter is not preservation of response magnitude, but preservation of each neuron's rank-order object preference under identity-preserving image transformations (e.g., clutter). Because IT neuronal responses often exhibit that response property, while neurons in earlier visual areas (e.g., V1) do not, we suggest that preserving the rank-order object preference regardless of clutter, rather than the response magnitude, more precisely describes the goal of individual neurons at the top of the ventral visual stream. PMID:19439676

  11. Multi-Stage System for Automatic Target Recognition

    NASA Technical Reports Server (NTRS)

    Chao, Tien-Hsin; Lu, Thomas T.; Ye, David; Edens, Weston; Johnson, Oliver

    2010-01-01

    A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feedforward back-propagation neural network (NN) is then trained to classify each feature vector and to remove false positives. The system parameter optimizations process has been developed to adapt to various targets and datasets. The objective was to design an efficient computer vision system that can learn to detect multiple targets in large images with unknown backgrounds. Because the target size is small relative to the image size in this problem, there are many regions of the image that could potentially contain the target. A cursory analysis of every region can be computationally efficient, but may yield too many false positives. On the other hand, a detailed analysis of every region can yield better results, but may be computationally inefficient. The multi-stage ATR system was designed to achieve an optimal balance between accuracy and computational efficiency by incorporating both models. The detection stage first identifies potential ROIs where the target may be present by performing a fast Fourier domain OT-MACH filter-based correlation. Because threshold for this stage is chosen with the goal of detecting all true positives, a number of false positives are also detected as ROIs. The verification stage then transforms the regions of interest into feature space, and eliminates false positives using an

  12. Training methodologies for dependent Speech Recognition (SR) systems

    NASA Astrophysics Data System (ADS)

    Miller, Richard L.

    1991-03-01

    An experiment was conducted to determine whether a dependent (SR) system would perform with different accuracies given different ways in which it was trained. The experiment used an SR system (Voice Navigator) which is based on Dragon Systems, Inc. (proprietary) technology. Fifteen subjects trained three different voice patterns each and conducted four tests to compile statistics about the recognition accuracy for each pattern. The experiment was successful and demonstrated that the training method used can have significant impact on the performance of a dependent SR system. This thesis discusses the research methodology, reviews and analyzes the data collected, and states conclusions drawn about the particular dependent SR system used in the experiment.

  13. The advantages of stereo vision in a face recognition system

    NASA Astrophysics Data System (ADS)

    Zheng, Yufeng; Blasch, Erik

    2014-06-01

    Humans can recognize a face with binocular vision, while computers typically use a single face image. It is known that the performance of face recognition (by a computer) can be improved using the score fusion of multimodal images and multiple algorithms. A question is: Can we apply stereo vision to a face recognition system? We know that human binocular vision has many advantages such as stereopsis (3D vision), binocular summation, and singleness of vision including fusion of binocular images (cyclopean image). For face recognition, a 3D face or 3D facial features are typically computed from a pair of stereo images. In human visual processes, the binocular summation and singleness of vision are similar as image fusion processes. In this paper, we propose an advanced face recognition system with stereo imaging capability, which is comprised of two 2-in-1 multispectral (visible and thermal) cameras and three recognition algorithms (circular Gaussian filter, face pattern byte, and linear discriminant analysis [LDA]). Specifically, we present and compare stereo fusion at three levels (images, features, and scores) by using stereo images (from left camera and right camera). Image fusion is achieved with three methods (Laplacian pyramid, wavelet transform, average); feature fusion is done with three logical operations (AND, OR, XOR); and score fusion is implemented with four classifiers (LDA, k-nearest neighbor, support vector machine, binomial logical regression). The system performance is measured by probability of correct classification (PCC) rate (reported as accuracy rate in this paper) and false accept rate (FAR). The proposed approaches were validated with a multispectral stereo face dataset from 105 subjects. Experimental results show that any type of stereo fusion can improve the PCC, meanwhile reduce the FAR. It seems that stereo image/feature fusion is superior to stereo score fusion in terms of recognition performance. Further score fusion after image

  14. Coarse-coded higher-order neural networks for PSRI object recognition. [position, scale, and rotation invariant

    NASA Technical Reports Server (NTRS)

    Spirkovska, Lilly; Reid, Max B.

    1993-01-01

    A higher-order neural network (HONN) can be designed to be invariant to changes in scale, translation, and inplane rotation. Invariances are built directly into the architecture of a HONN and do not need to be learned. Consequently, fewer training passes and a smaller training set are required to learn to distinguish between objects. The size of the input field is limited, however, because of the memory required for the large number of interconnections in a fully connected HONN. By coarse coding the input image, the input field size can be increased to allow the larger input scenes required for practical object recognition problems. We describe a coarse coding technique and present simulation results illustrating its usefulness and its limitations. Our simulations show that a third-order neural network can be trained to distinguish between two objects in a 4096 x 4096 pixel input field independent of transformations in translation, in-plane rotation, and scale in less than ten passes through the training set. Furthermore, we empirically determine the limits of the coarse coding technique in the object recognition domain.

  15. Speech recognition control system and method

    SciTech Connect

    Lemelson, J.H.

    1986-08-12

    This invention relates to a system and method for weighing articles and quantities of material wherein computing functions are performed to effect calculations and the control of a visual presentation means such as a display or printer or the generation of signals for use in recording a transaction. In particular, the invention relates to such a weighing and computing apparatus and method which operates or varies in response to speech signals generated by selected words of speech spoken into a microphone by an operator of the apparatus. It is known in the art to electronically detect the weight of articles and containers of material and to generate electrical signals which are indicative of such weight. It is also known to effect a computation with respect to such signals and additional signals generated by manually operating selected keys of a keyboard wherein the additional signals represent one or more additional variables which must be divided into or multiplied by the numerical representation of the weights of articles weighed by such apparatus.

  16. Time and timing in the acoustic recognition system of crickets

    PubMed Central

    Hennig, R. Matthias; Heller, Klaus-Gerhard; Clemens, Jan

    2014-01-01

    The songs of many insects exhibit precise timing as the result of repetitive and stereotyped subunits on several time scales. As these signals encode the identity of a species, time and timing are important for the recognition system that analyzes these signals. Crickets are a prominent example as their songs are built from sound pulses that are broadcast in a long trill or as a chirped song. This pattern appears to be analyzed on two timescales, short and long. Recent evidence suggests that song recognition in crickets relies on two computations with respect to time; a short linear-nonlinear (LN) model that operates as a filter for pulse rate and a longer integration time window for monitoring song energy over time. Therefore, there is a twofold role for timing. A filter for pulse rate shows differentiating properties for which the specific timing of excitation and inhibition is important. For an integrator, however, the duration of the time window is more important than the precise timing of events. Here, we first review evidence for the role of LN-models and integration time windows for song recognition in crickets. We then parameterize the filter part by Gabor functions and explore the effects of duration, frequency, phase, and offset as these will correspond to differently timed patterns of excitation and inhibition. These filter properties were compared with known preference functions of crickets and katydids. In a comparative approach, the power for song discrimination by LN-models was tested with the songs of over 100 cricket species. It is demonstrated how the acoustic signals of crickets occupy a simple 2-dimensional space for song recognition that arises from timing, described by a Gabor function, and time, the integration window. Finally, we discuss the evolution of recognition systems in insects based on simple sensory computations. PMID:25161622

  17. Mapping parahippocampal systems for recognition and recency memory in the absence of the rat hippocampus

    PubMed Central

    Kinnavane, L; Amin, E; Horne, M; Aggleton, J P

    2014-01-01

    The present study examined immediate-early gene expression in the perirhinal cortex of rats with hippocampal lesions. The goal was to test those models of recognition memory which assume that the perirhinal cortex can function independently of the hippocampus. The c-fos gene was targeted, as its expression in the perirhinal cortex is strongly associated with recognition memory. Four groups of rats were examined. Rats with hippocampal lesions and their surgical controls were given either a recognition memory task (novel vs. familiar objects) or a relative recency task (objects with differing degrees of familiarity). Perirhinal Fos expression in the hippocampal-lesioned groups correlated with both recognition and recency performance. The hippocampal lesions, however, had no apparent effect on overall levels of perirhinal or entorhinal cortex c-fos expression in response to novel objects, with only restricted effects being seen in the recency condition. Network analyses showed that whereas the patterns of parahippocampal interactions were differentially affected by novel or familiar objects, these correlated networks were not altered by hippocampal lesions. Additional analyses in control rats revealed two modes of correlated medial temporal activation. Novel stimuli recruited the pathway from the lateral entorhinal cortex (cortical layer II or III) to hippocampal field CA3, and thence to CA1. Familiar stimuli recruited the direct pathway from the lateral entorhinal cortex (principally layer III) to CA1. The present findings not only reveal the independence from the hippocampus of some perirhinal systems associated with recognition memory, but also show how novel stimuli engage hippocampal subfields in qualitatively different ways from familiar stimuli. PMID:25264133

  18. Automatic Detection and Recognition of Man-Made Objects in High Resolution Remote Sensing Images Using Hierarchical Semantic Graph Model

    NASA Astrophysics Data System (ADS)

    Sun, X.; Thiele, A.; Hinz, S.; Fu, K.

    2013-05-01

    In this paper, we propose a hierarchical semantic graph model to detect and recognize man-made objects in high resolution remote sensing images automatically. Following the idea of part-based methods, our model builds a hierarchical possibility framework to explore both the appearance information and semantic relationships between objects and background. This multi-levels structure is promising to enable a more comprehensive understanding of natural scenes. After training local classifiers to calculate parts properties, we use belief propagation to transmit messages quantitatively, which could enhance the utilization of spatial constrains existed in images. Besides, discriminative learning and generative learning are combined interleavely in the inference procedure, to improve the training error and recognition efficiency. The experimental results demonstrate that this method is able to detect manmade objects in complicated surroundings with satisfactory precision and robustness.

  19. Correcting scale drift by object recognition in single-camera SLAM.

    PubMed

    Botterill, Tom; Mills, Steven; Green, Richard

    2013-12-01

    This paper proposes a novel solution to the problem of scale drift in single-camera simultaneous localization and mapping, based on recognizing and measuring objects. When reconstructing the trajectory of a camera moving in an unknown environment, the scale of the environment, and equivalently the speed of the camera, is obtained by accumulating relative scale estimates over sequences of frames. This leads to scale drift: errors in scale accumulate over time. The proposed solution is to learn the classes of objects that appear throughout the environment and to use measurements of the size of these objects to improve the scale estimate. A bag-of-words-based scheme to learn object classes, to recognize object instances, and to use these observations to correct scale drift is described and is demonstrated reducing accumulated errors by 64% while navigating for 2.5 km through a dynamic outdoor environment.

  20. Correcting scale drift by object recognition in single-camera SLAM.

    PubMed

    Botterill, Tom; Mills, Steven; Green, Richard

    2013-12-01

    This paper proposes a novel solution to the problem of scale drift in single-camera simultaneous localization and mapping, based on recognizing and measuring objects. When reconstructing the trajectory of a camera moving in an unknown environment, the scale of the environment, and equivalently the speed of the camera, is obtained by accumulating relative scale estimates over sequences of frames. This leads to scale drift: errors in scale accumulate over time. The proposed solution is to learn the classes of objects that appear throughout the environment and to use measurements of the size of these objects to improve the scale estimate. A bag-of-words-based scheme to learn object classes, to recognize object instances, and to use these observations to correct scale drift is described and is demonstrated reducing accumulated errors by 64% while navigating for 2.5 km through a dynamic outdoor environment. PMID:24273146

  1. Effects of the Similarity and Dissimilarity between Familiarization and Test Objects on Recognition Memory in Infants Following Unimodal and Bimodal Familiarization.

    ERIC Educational Resources Information Center

    Rolfe, Sharne A.; Day, R. H.

    1981-01-01

    Two experiments were conducted to investigate six-month-old infants' recognition memory for the shape of an object following unimodal (visual) and bimodal (visual and haptic) familiarization. Visual recognition memory was evident only when the conditions of familiarization and testing were identical. Two possible explanations are presented and…

  2. The anatomy of object recognition--visual form agnosia caused by medial occipitotemporal stroke.

    PubMed

    Karnath, Hans-Otto; Rüter, Johannes; Mandler, André; Himmelbach, Marc

    2009-05-01

    The influential model on visual information processing by Milner and Goodale (1995) has suggested a dissociation between action- and perception-related processing in a dorsal versus ventral stream projection. It was inspired substantially by the observation of a double dissociation of disturbed visual action versus perception in patients with optic ataxia on the one hand and patients with visual form agnosia (VFA) on the other. Unfortunately, almost all cases with VFA reported so far suffered from inhalational intoxication, the majority with carbon monoxide (CO). Since CO induces a diffuse and widespread pattern of neuronal and white matter damage throughout the whole brain, precise conclusions from these patients with VFA on the selective role of ventral stream structures for shape and orientation perception were difficult. Here, we report patient J.S., who demonstrated VFA after a well circumscribed brain lesion due to stroke etiology. Like the famous patient D.F. with VFA after CO intoxication studied by Milner, Goodale, and coworkers (Goodale et al., 1991, 1994; Milner et al., 1991; Servos et al., 1995; Mon-Williams et al., 2001a,b; Wann et al., 2001; Westwood et al., 2002; McIntosh et al., 2004; Schenk and Milner, 2006), J.S. showed an obvious dissociation between disturbed visual perception of shape and orientation information on the one side and preserved visuomotor abilities based on the same information on the other. In both hemispheres, damage primarily affected the fusiform and the lingual gyri as well as the adjacent posterior cingulate gyrus. We conclude that these medial structures of the ventral occipitotemporal cortex are integral for the normal flow of shape and of contour information into the ventral stream system allowing to recognize objects.

  3. Speech recognition systems on the Cell Broadband Engine

    SciTech Connect

    Liu, Y; Jones, H; Vaidya, S; Perrone, M; Tydlitat, B; Nanda, A

    2007-04-20

    In this paper we describe our design, implementation, and first results of a prototype connected-phoneme-based speech recognition system on the Cell Broadband Engine{trademark} (Cell/B.E.). Automatic speech recognition decodes speech samples into plain text (other representations are possible) and must process samples at real-time rates. Fortunately, the computational tasks involved in this pipeline are highly data-parallel and can receive significant hardware acceleration from vector-streaming architectures such as the Cell/B.E. Identifying and exploiting these parallelism opportunities is challenging, but also critical to improving system performance. We observed, from our initial performance timings, that a single Cell/B.E. processor can recognize speech from thousands of simultaneous voice channels in real time--a channel density that is orders-of-magnitude greater than the capacity of existing software speech recognizers based on CPUs (central processing units). This result emphasizes the potential for Cell/B.E.-based speech recognition and will likely lead to the future development of production speech systems using Cell/B.E. clusters.

  4. Mice deficient for striatal Vesicular Acetylcholine Transporter (VAChT) display impaired short-term but normal long-term object recognition memory.

    PubMed

    Palmer, Daniel; Creighton, Samantha; Prado, Vania F; Prado, Marco A M; Choleris, Elena; Winters, Boyer D

    2016-09-15

    Substantial evidence implicates Acetylcholine (ACh) in the acquisition of object memories. While most research has focused on the role of the cholinergic basal forebrain and its cortical targets, there are additional cholinergic networks that may contribute to object recognition. The striatum contains an independent cholinergic network comprised of interneurons. In the current study, we investigated the role of this cholinergic signalling in object recognition using mice deficient for Vesicular Acetylcholine Transporter (VAChT) within interneurons of the striatum. We tested whether these striatal VAChT(D2-Cre-flox/flox) mice would display normal short-term (5 or 15min retention delay) and long-term (3h retention delay) object recognition memory. In a home cage object recognition task, male and female VAChT(D2-Cre-flox/flox) mice were impaired selectively with a 15min retention delay. When tested on an object location task, VAChT(D2-Cre-flox/flox) mice displayed intact spatial memory. Finally, when object recognition was tested in a Y-shaped apparatus, designed to minimize the influence of spatial and contextual cues, only females displayed impaired recognition with a 5min retention delay, but when males were challenged with a 15min retention delay, they were also impaired; neither males nor females were impaired with the 3h delay. The pattern of results suggests that striatal cholinergic transmission plays a role in the short-term memory for object features, but not spatial location. PMID:27233822

  5. Mice deficient for striatal Vesicular Acetylcholine Transporter (VAChT) display impaired short-term but normal long-term object recognition memory.

    PubMed

    Palmer, Daniel; Creighton, Samantha; Prado, Vania F; Prado, Marco A M; Choleris, Elena; Winters, Boyer D

    2016-09-15

    Substantial evidence implicates Acetylcholine (ACh) in the acquisition of object memories. While most research has focused on the role of the cholinergic basal forebrain and its cortical targets, there are additional cholinergic networks that may contribute to object recognition. The striatum contains an independent cholinergic network comprised of interneurons. In the current study, we investigated the role of this cholinergic signalling in object recognition using mice deficient for Vesicular Acetylcholine Transporter (VAChT) within interneurons of the striatum. We tested whether these striatal VAChT(D2-Cre-flox/flox) mice would display normal short-term (5 or 15min retention delay) and long-term (3h retention delay) object recognition memory. In a home cage object recognition task, male and female VAChT(D2-Cre-flox/flox) mice were impaired selectively with a 15min retention delay. When tested on an object location task, VAChT(D2-Cre-flox/flox) mice displayed intact spatial memory. Finally, when object recognition was tested in a Y-shaped apparatus, designed to minimize the influence of spatial and contextual cues, only females displayed impaired recognition with a 5min retention delay, but when males were challenged with a 15min retention delay, they were also impaired; neither males nor females were impaired with the 3h delay. The pattern of results suggests that striatal cholinergic transmission plays a role in the short-term memory for object features, but not spatial location.

  6. Vision-based obstacle recognition system for automated lawn mower robot development

    NASA Astrophysics Data System (ADS)

    Mohd Zin, Zalhan; Ibrahim, Ratnawati

    2011-06-01

    Digital image processing techniques (DIP) have been widely used in various types of application recently. Classification and recognition of a specific object using vision system require some challenging tasks in the field of image processing and artificial intelligence. The ability and efficiency of vision system to capture and process the images is very important for any intelligent system such as autonomous robot. This paper gives attention to the development of a vision system that could contribute to the development of an automated vision based lawn mower robot. The works involve on the implementation of DIP techniques to detect and recognize three different types of obstacles that usually exist on a football field. The focus was given on the study on different types and sizes of obstacles, the development of vision based obstacle recognition system and the evaluation of the system's performance. Image processing techniques such as image filtering, segmentation, enhancement and edge detection have been applied in the system. The results have shown that the developed system is able to detect and recognize various types of obstacles on a football field with recognition rate of more 80%.

  7. Words Jump-Start Vision: A Label Advantage in Object Recognition.

    PubMed

    Boutonnet, Bastien; Lupyan, Gary

    2015-06-24

    People use language to shape each other's behavior in highly flexible ways. Effects of language are often assumed to be "high-level" in that, whereas language clearly influences reasoning, decision making, and memory, it does not influence low-level visual processes. Here, we test the prediction that words are able to provide top-down guidance at the very earliest stages of visual processing by acting as powerful categorical cues. We investigated whether visual processing of images of familiar animals and artifacts was enhanced after hearing their name (e.g., "dog") compared with hearing an equally familiar and unambiguous nonverbal sound (e.g., a dog bark) in 14 English monolingual speakers. Because the relationship between words and their referents is categorical, we expected words to deploy more effective categorical templates, allowing for more rapid visual recognition. By recording EEGs, we were able to determine whether this label advantage stemmed from changes to early visual processing or later semantic decision processes. The results showed that hearing a wor